Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.

Sunday, July 23rd of 2017, a 22-year-old Uber driver Guilherme e Silva Maia was waiting for a passenger at Ancuri, a peripheral neighbourhood of Fortaleza, when two unknown assailants shot and killed him. Authorities claimed that the murderers had mistaken him for a member of their rival gang, as his car’s dark windows were closed, disobeying informal rules printed on the walls upon entering the neighbourhood: “Remove helmet. Lower car windows. Turn on light inside the car”. Three years later, on October 21st of 2020, a group of drug dealers shot Christiano Coimbra, an O Globonewspaper employee, after having entered the community of Cidade Alta, following a transit app suggestion to bypass traffic on Avenida Brasil, a major artery of Rio de Janeiro.

Episodes like these tend to reignite discussion about the need for an “avoid dangerous areas” feature on these apps, which is reportedly available on Waze for some cities in Brazil. On Waze, these areas are supposed to be collaboratively marked, just as with the other alerts in the app. But in the future, this task might fall to an algorithm,  with no guarantee that, through machine learning, it won’t simply label informal settlements as violent spaces. Handing over the control of marking potentially violent areas to new stakeholders (such as Uber or Waze) without the moderation of democratic state institutions represents a major shift in the territorial governance of cities in the Global South. This shift may end up substituting the pursuit of a good, inclusive, public mapping system, for one governed at the whim of corporate interests, whose agenda is not often convergent with those of vulnerable residents of informal settlements. And by “good, inclusive, public mapping”, we mean a system that is attentive to the voices of the residents of the most vulnerable neighbourhoods, capable of supporting a democratic urban decision-making process.

The cases of Silva Maia and Coimbra underline the importance of identifying who controls and moderates spatial information. Algorithms act according to what they are trained for, and human beings are the ones training them. Therefore, when algorithms are clearly reinforcing existing inequalities, it is crucial to question who writes these algorithms, and in whose interests they are writing them. More importantly, these people should be held accountable for the socio-spatial effects of their products. 

The last decade has seen a great proliferation of digital georeferencing tools based on urban mapping algorithms: from spatial data banks such as Google Maps and Bing to location collection functionality on social media apps such as Whatsapp and Twitter. This might constitute an opportunity for boosting democratic decision-making in the urbanisation process, as the technology enables people without technical training to share maps tailored to their needs. However, this is a two-way street: citizens map but are also being mapped. A person’s location data is a valuable resource, which has been commercialised largely under the noses of both individual citizens and their representative democratic institutions. 

For decades mapping was an expensive, time-consuming and hand-made process. Therefore, in the cartographic field, power was concentrated in the hands of a few people, from colonisers in the early centuries to, more recently, technicians usually hired by the state. Before the popularisation of Information and Communication Technologies (ICTs) and Geographic Information Systems (GIS),  residents of the unmapped spaces did not officially exist, as they were absent from planning agencies’ databases. At the end of the day, this absence denied them certain opportunities afforded to those with the status of full citizenship: including government investment in their neighbourhoods and welfare. The image below from the community of Lauro Vieira Chaves illustrates a typical situation of major metropolises in the Northeast of Brazil where the subdivision design proposal approved by local planning agencies differed from the actually existing built environment. Here, urban development had occurred informally, bypassing existing land use control mechanisms because they presupposed plot sizes and streets much larger and wider than an average urban dweller could afford. Public utility companies would often deny services to settlements that did not conform to these plot sizes, on the grounds that they were illegal, and, in any case, dismissing them as exceptional cases. As such, in the same image, we see that the network of sewer systems does not cover the informally opened streets.