This chapter charts the transition from an analogue to a digital world, its effect on data footprints and shadows, and the growth of data brokers and government use of data. The World Wide Web (WWW) started to change things by making information accessible across the Internet through an easy-to-use, intuitive graphical interface. Using the Internet, people started leaving digital traces. In their everyday lives, their digital shadows were also growing through the use of debit, credit, and store loyalty cards, and captured in government databases which were increasingly digital. Running tandem to the creation of digital lifestyles was the datafication of everyday life. This was evident in a paper which examined the various ways in which digital data was being generated and tracked using indexical codes about people, but also objects, transactions, interactions, and territories, and how these data were being used to govern people and manage organizations. Today, people live in a world of continuous data production, since smart systems generate data in real time.
{"title":"Traces and Shadows","authors":"Rob Kitchin","doi":"10.2307/j.ctv1c9hmnq.18","DOIUrl":"https://doi.org/10.2307/j.ctv1c9hmnq.18","url":null,"abstract":"This chapter charts the transition from an analogue to a digital world, its effect on data footprints and shadows, and the growth of data brokers and government use of data. The World Wide Web (WWW) started to change things by making information accessible across the Internet through an easy-to-use, intuitive graphical interface. Using the Internet, people started leaving digital traces. In their everyday lives, their digital shadows were also growing through the use of debit, credit, and store loyalty cards, and captured in government databases which were increasingly digital. Running tandem to the creation of digital lifestyles was the datafication of everyday life. This was evident in a paper which examined the various ways in which digital data was being generated and tracked using indexical codes about people, but also objects, transactions, interactions, and territories, and how these data were being used to govern people and manage organizations. Today, people live in a world of continuous data production, since smart systems generate data in real time.","PeriodicalId":446623,"journal":{"name":"Data Lives","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133679310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This chapter addresses how profiling and social sorting shape consumption and entertainment through recommendations and nudges. It looks at a conversation between a man and his grandchild while they were deciding what to watch. The grandchild mentioned a movie which Netflix says is a 96-per cent match to his tastes. He explains that Netflix tracks what he watches and whether he likes something, then suggests other programmes it thinks he might like. The man then talks about the importance of pushing one's boundaries and getting exposed to new stories, ideas, and genres to learn stuff and cultivate new tastes. When the conversation turned to the news, the grandchild mentions Facebook, which he claims is another thing that only shows him what it wants him to see. He also talks about targeted ads and algorithms.
{"title":"Recommended Life","authors":"Rob Kitchin","doi":"10.2307/j.ctv1c9hmnq.19","DOIUrl":"https://doi.org/10.2307/j.ctv1c9hmnq.19","url":null,"abstract":"This chapter addresses how profiling and social sorting shape consumption and entertainment through recommendations and nudges. It looks at a conversation between a man and his grandchild while they were deciding what to watch. The grandchild mentioned a movie which Netflix says is a 96-per cent match to his tastes. He explains that Netflix tracks what he watches and whether he likes something, then suggests other programmes it thinks he might like. The man then talks about the importance of pushing one's boundaries and getting exposed to new stories, ideas, and genres to learn stuff and cultivate new tastes. When the conversation turned to the news, the grandchild mentions Facebook, which he claims is another thing that only shows him what it wants him to see. He also talks about targeted ads and algorithms.","PeriodicalId":446623,"journal":{"name":"Data Lives","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128860906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This chapter details a blind date between two researchers who have very different notions about the nature of data and the ethos and practices of science. One is an electronic engineer, while the other is an anthropologist. The anthropologist studies how digital technology is built and used, examining the politics and praxes of some start-up companies who were developing new apps. Meanwhile, the electronic engineer works on a sound-sensing network for monitoring and modelling background noise across the city. The chapter then looks at their debate on data creation and collection. The anthropologist makes a point about scientific practice, arguing that the electronic engineer is practising mechanical objectivity — trying to minimize biases, errors, calibration issues, and so on — but it is still set up in their vision, based on their education and experience, and compromising for circumstance. Thus, they are still making choices that influence the outcome.
{"title":"Blind Data","authors":"Rob Kitchin","doi":"10.2307/j.ctv1c9hmnq.6","DOIUrl":"https://doi.org/10.2307/j.ctv1c9hmnq.6","url":null,"abstract":"This chapter details a blind date between two researchers who have very different notions about the nature of data and the ethos and practices of science. One is an electronic engineer, while the other is an anthropologist. The anthropologist studies how digital technology is built and used, examining the politics and praxes of some start-up companies who were developing new apps. Meanwhile, the electronic engineer works on a sound-sensing network for monitoring and modelling background noise across the city. The chapter then looks at their debate on data creation and collection. The anthropologist makes a point about scientific practice, arguing that the electronic engineer is practising mechanical objectivity — trying to minimize biases, errors, calibration issues, and so on — but it is still set up in their vision, based on their education and experience, and compromising for circumstance. Thus, they are still making choices that influence the outcome.","PeriodicalId":446623,"journal":{"name":"Data Lives","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129368754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This chapter investigates data interoperability and the difficulties in harmonizing data across jurisdictions, using Ireland/Northern Ireland and Metropolitan Boston as case studies. In the wake of the Good Friday Agreement and the peace process in Northern Ireland, cooperation between public sector bodies in the North and South had increased enormously. However, there was a dearth of cross-border datasets to formulate policy and inform decision-making. It quickly became apparent why there were few, detailed cross-border data visualizations and maps — it was very difficult to create single, common datasets. What was needed was data harmonization where agencies worked together to create comparable datasets. Metropolitan Boston has 101 local government departments, which means it has 101 data regimes. What this means is that, with the exception of data required for state/federal reporting, it is impossible to join datasets together to create comparable metro-wide datasets. This has a number of consequences, reducing spatial intelligence about the characteristics and performance of the city-region, fostering back-to-back planning, limiting potential data-driven innovations to urban governance and management, and stifling the benefits of open data.
{"title":"Harmonizing Data is Hard","authors":"Rob Kitchin","doi":"10.2307/j.ctv1c9hmnq.11","DOIUrl":"https://doi.org/10.2307/j.ctv1c9hmnq.11","url":null,"abstract":"This chapter investigates data interoperability and the difficulties in harmonizing data across jurisdictions, using Ireland/Northern Ireland and Metropolitan Boston as case studies. In the wake of the Good Friday Agreement and the peace process in Northern Ireland, cooperation between public sector bodies in the North and South had increased enormously. However, there was a dearth of cross-border datasets to formulate policy and inform decision-making. It quickly became apparent why there were few, detailed cross-border data visualizations and maps — it was very difficult to create single, common datasets. What was needed was data harmonization where agencies worked together to create comparable datasets. Metropolitan Boston has 101 local government departments, which means it has 101 data regimes. What this means is that, with the exception of data required for state/federal reporting, it is impossible to join datasets together to create comparable metro-wide datasets. This has a number of consequences, reducing spatial intelligence about the characteristics and performance of the city-region, fostering back-to-back planning, limiting potential data-driven innovations to urban governance and management, and stifling the benefits of open data.","PeriodicalId":446623,"journal":{"name":"Data Lives","volume":"6 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123738293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This chapter looks at an argument between two researchers concerning the epistemology, methodology, and ethics of data science versus traditional science in studying fertility. One of the researchers questions the other's use of Twitter data to examine fertility. The other researcher's defence is that Twitter data can be used to calculate a proxy fertility rate, comparing rates of women with and without children, looking at family changes, mapping geographic patterns of the tweets, but they were only partially using the data for this. They were mainly interested in soft measures concerning fertility, such as attitudes, values, feelings, and intentions. And about related issues such as family planning, abortion, and overpopulation. In particular, they can get a sense of sentiment: whether people are positive or negative about parenthood, whether they are tired, overjoyed, or depressed. However, the first researcher was not convinced because their approach to understanding fertility starts from a very different place — one driven by theory and hypotheses.
{"title":"So More Trumps Better?","authors":"Rob Kitchin","doi":"10.2307/j.ctv1c9hmnq.14","DOIUrl":"https://doi.org/10.2307/j.ctv1c9hmnq.14","url":null,"abstract":"This chapter looks at an argument between two researchers concerning the epistemology, methodology, and ethics of data science versus traditional science in studying fertility. One of the researchers questions the other's use of Twitter data to examine fertility. The other researcher's defence is that Twitter data can be used to calculate a proxy fertility rate, comparing rates of women with and without children, looking at family changes, mapping geographic patterns of the tweets, but they were only partially using the data for this. They were mainly interested in soft measures concerning fertility, such as attitudes, values, feelings, and intentions. And about related issues such as family planning, abortion, and overpopulation. In particular, they can get a sense of sentiment: whether people are positive or negative about parenthood, whether they are tired, overjoyed, or depressed. However, the first researcher was not convinced because their approach to understanding fertility starts from a very different place — one driven by theory and hypotheses.","PeriodicalId":446623,"journal":{"name":"Data Lives","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129205102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This chapter charts how a group of citizens seek to challenge systemic and institutional racism within their city by building their own datasets and tools. It looks at how some computer programmers created black data — data about the murder of black lives, data about systemic, institutional racism, and data that demands justice. The result is a Mapping Police Violence database. The database maps the deaths of people killed by the police by district, most of which happened in black neighbourhoods. This initiative then grew into a black atlas of the city, incorporating crime data, census data, and some housing and welfare data.
{"title":"Black Data Matter","authors":"Rob Kitchin","doi":"10.2307/j.ctv1c9hmnq.29","DOIUrl":"https://doi.org/10.2307/j.ctv1c9hmnq.29","url":null,"abstract":"This chapter charts how a group of citizens seek to challenge systemic and institutional racism within their city by building their own datasets and tools. It looks at how some computer programmers created black data — data about the murder of black lives, data about systemic, institutional racism, and data that demands justice. The result is a Mapping Police Violence database. The database maps the deaths of people killed by the police by district, most of which happened in black neighbourhoods. This initiative then grew into a black atlas of the city, incorporating crime data, census data, and some housing and welfare data.","PeriodicalId":446623,"journal":{"name":"Data Lives","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114162940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This chapter explores what kind of data future we want to create and strategies for realizing our visions. It highlights the need to enact 'a digital ethics of care', and to claim and assert 'data sovereignty'. An ethics of digital care is practising reciprocal and nonreciprocal care with respect to digital life, including data practices: that we care for ourselves and others in ways that we expect to be treated, and are supportive and promote wellbeing and not exploitative. This means acting in moral ways with respect to the generation and use of data. Accompanying an ethics of digital care should be digital rights and entitlements. Data sovereignty is the idea that we should have some authority and control over data that relates to us and that other individuals, companies, and states should recognize the legitimacy of that sovereignty. In other words, we should have a say in what data are generated about us and have an ownership stake in those data that dictates how they are treated and shared, and for what purpose they can be used.
{"title":"Data Futures","authors":"Rob Kitchin","doi":"10.2307/j.ctv1c9hmnq.31","DOIUrl":"https://doi.org/10.2307/j.ctv1c9hmnq.31","url":null,"abstract":"This chapter explores what kind of data future we want to create and strategies for realizing our visions. It highlights the need to enact 'a digital ethics of care', and to claim and assert 'data sovereignty'. An ethics of digital care is practising reciprocal and nonreciprocal care with respect to digital life, including data practices: that we care for ourselves and others in ways that we expect to be treated, and are supportive and promote wellbeing and not exploitative. This means acting in moral ways with respect to the generation and use of data. Accompanying an ethics of digital care should be digital rights and entitlements. Data sovereignty is the idea that we should have some authority and control over data that relates to us and that other individuals, companies, and states should recognize the legitimacy of that sovereignty. In other words, we should have a say in what data are generated about us and have an ownership stake in those data that dictates how they are treated and shared, and for what purpose they can be used.","PeriodicalId":446623,"journal":{"name":"Data Lives","volume":"131 22","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131745796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This chapter addresses the life of COVID-19 data, how it has been used to reshape our daily lives by directing intervention measures, and how new data-driven technologies have been deployed to try and help tackle the spread of the coronavirus. Specifically, it examines infection and death rates and the use of surveillance technologies designed to trace contacts, monitor movement, and regulate people's behaviour. The use of these technologies raised questions and active debate concerning the data life cycle and their effects on civil liberties and governmentality. Indeed, most of the critical analysis of contact tracing apps focused on its potential infringement of civil liberties, particularly privacy, since they require fine-grained knowledge about social networks and health status and, for some, location. The concern was that intimate details about a person's life would be shared with the state without sufficient data protection measures that would foreclose data re/misuse and ensure that data would be deleted after 14 days (at which point it becomes redundant) or stored indefinitely.
{"title":"A Matter of Life and Death","authors":"A. Carr","doi":"10.2307/j.ctv1c9hmnq.30","DOIUrl":"https://doi.org/10.2307/j.ctv1c9hmnq.30","url":null,"abstract":"This chapter addresses the life of COVID-19 data, how it has been used to reshape our daily lives by directing intervention measures, and how new data-driven technologies have been deployed to try and help tackle the spread of the coronavirus. Specifically, it examines infection and death rates and the use of surveillance technologies designed to trace contacts, monitor movement, and regulate people's behaviour. The use of these technologies raised questions and active debate concerning the data life cycle and their effects on civil liberties and governmentality. Indeed, most of the critical analysis of contact tracing apps focused on its potential infringement of civil liberties, particularly privacy, since they require fine-grained knowledge about social networks and health status and, for some, location. The concern was that intimate details about a person's life would be shared with the state without sufficient data protection measures that would foreclose data re/misuse and ensure that data would be deleted after 14 days (at which point it becomes redundant) or stored indefinitely.","PeriodicalId":446623,"journal":{"name":"Data Lives","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134257335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This chapter focuses on the role of finance and the politics of collaboration, charting the development of the Digital Repository of Ireland (DRI). DRI have been beset with institutional politics concerning its framing, development, and operation. The future funding issue was just the latest example in a long list of fraught exchanges that could be traced back to its original conception and funding mechanism. The DRI was born out of a funding opportunity, but seemed destined to die due to a funding failure. Without a political solution, the data life cycle would turn full circle much more quickly than initially anticipated. Unless there is a means of covering the costs for labour, equipment and other essential inputs, data are not generated or stored, and thus cannot be used or shared. Even in open data projects, the data might be free to use but they were not free to create, or to process and host.
{"title":"Hustling for Funding","authors":"Rob Kitchin","doi":"10.2307/j.ctv1c9hmnq.15","DOIUrl":"https://doi.org/10.2307/j.ctv1c9hmnq.15","url":null,"abstract":"This chapter focuses on the role of finance and the politics of collaboration, charting the development of the Digital Repository of Ireland (DRI). DRI have been beset with institutional politics concerning its framing, development, and operation. The future funding issue was just the latest example in a long list of fraught exchanges that could be traced back to its original conception and funding mechanism. The DRI was born out of a funding opportunity, but seemed destined to die due to a funding failure. Without a political solution, the data life cycle would turn full circle much more quickly than initially anticipated. Unless there is a means of covering the costs for labour, equipment and other essential inputs, data are not generated or stored, and thus cannot be used or shared. Even in open data projects, the data might be free to use but they were not free to create, or to process and host.","PeriodicalId":446623,"journal":{"name":"Data Lives","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134482051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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