Pub Date : 2021-05-07DOI: 10.4337/9781789909791.00013
Les Dolega
Town centres form the core of many urban areas and are characterized by clustering of various types of socio-economic activities with retail and related services being fundamental. They can be viewed as complex systems that constantly evolve, and therefore their composition and spatial extent is likely to expand or contract over time. Although it has been argued that depicting retail agglomerations for a national extent, is challenging, the classification of shopping destinations and delineation of their spatial extent is essential to gaining a better understanding of the relationship between use of retail space and changing consumer behaviour. These challenges have been approached as follows: Firstly, a new automated method for identification of retail agglomerations within Great Britain was proposed. By employing new forms of data at individual business level and application of a bespoke DBSCAN method over 3,000 retail centres have been identified. Secondly, delineation of catchment areas for those retail centres based on a mixed-method approach linked to their function. A Huff spatial interaction model was used to obtain catchment extends for convenience retail destination and drive times method for the higher order comparison retail destinations. Finally, to address the shortcomings of the early attempts to classify clusters of shopping activity that were closely linked to a measure of hierarchical status and involved two-dimensional scoring of retail centres from “high†to “low†, a new multidimensional typology of retail and consumption spaces was developed. Non-hierarchical clustering techniques were used to develop an understanding of consumption spaces in terms of four dimensions derived from the literature: a centre’s composition, its diversity, size and function, and its economic health. There seems to be a consensus that such more comprehensive classifications that capture the interrelationship between supply and demand for retailing services, would help to deliver more effective insights into changing role of retailing and consumer services in urban areas across space and through time and will have implicationns for a variety of stakeholders
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Pub Date : 2021-05-07DOI: 10.4337/9781789909791.00007
M. Alexander
Understanding migration patterns and how they change over time has important implications for understanding broader population trends, effectively designing policy and allocating resources. However, data on migration movements are often lacking, and those that do exist are not produced in a timely manner. Social media data offer new opportunities to provide more up-to-date demographic estimates and to complement more-traditional data sources. Facebook, for example, can be thought of as a large digital census that is regularly updated. However, its users are not representative of the underlying population, thus using the data without appropriate adjustments would lead to biased results. This chapter discusses the use of social media advertising data to estimate migration over time. A statistical framework for combining traditional data sources and the social media data is presented, which emphasizes the importance of three main components: adjusting for non-representativeness in the social media data; incorporating historical information from reliable demographic data; and accounting for different errors in each data source. The framework is illustrated through an example that uses data from Facebook’s advertising platform to estimate migrant stocks in North America.
{"title":"Using social media advertising data to estimate migration trends over time","authors":"M. Alexander","doi":"10.4337/9781789909791.00007","DOIUrl":"https://doi.org/10.4337/9781789909791.00007","url":null,"abstract":"Understanding migration patterns and how they change over time has important implications for understanding broader population trends, effectively designing policy and allocating resources. However, data on migration movements are often lacking, and those that do exist are not produced in a timely manner. Social media data offer new opportunities to provide more up-to-date demographic estimates and to complement more-traditional data sources. Facebook, for example, can be thought of as a large digital census that is regularly updated. However, its users are not representative of the underlying population, thus using the data without appropriate adjustments would lead to biased results. This chapter discusses the use of social media advertising data to estimate migration over time. A statistical framework for combining traditional data sources and the social media data is presented, which emphasizes the importance of three main components: adjusting for non-representativeness in the social media data; incorporating historical information from reliable demographic data; and accounting for different errors in each data source. The framework is illustrated through an example that uses data from Facebook’s advertising platform to estimate migrant stocks in North America.","PeriodicalId":118028,"journal":{"name":"Big Data Applications in Geography and Planning","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133400265","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}
Pub Date : 2021-05-07DOI: 10.4337/9781789909791.00006
M. Birkin, G. Clarke, J. Corcoran, R. Stimson
This chapter introduces the book ‘Big Data Applications in Geography and Planning: An Essential Companion’, which showcases applications of big data in human geography and urban planning. First we provide, as editors, some background on our own experience of dealing with big data and applied spatial modelling through various large-scale, international Government intiatives in both the UK and Australia. Then we review the big debates on using big data by focusing on three core areas and arguing that the material in the chapters of this book will make a significant contribution to each of these issues. The first debate focuses on the nature of theory building and the analytical techniques needed to process and analyse big data. We note here that all the chapters in the book discuss spatial data analysis in a big data environment. Second, we discuss issues surrounding data quality, data cleaning and data being fit for purpose. The variety of data used in the applications in the book should be a source of some consolation here to those particularly concerned with representation. If one source is heavily skewed toward a particular activity or sub-group we argue that this can be compensated by another source which has different characteristics. This is demonstrated in many chapters of the book. This discussion also considers ethics and concerns around confidentiality. Finally, but importantly, we recognise that proponents of big data still need to win over many sceptics concerning the contribution that the new data can make to traditional social science problems. How can big data supplement or even replace traditional survey data in the future? Although the literature is awash with articles discussing issues around big data we argue that there are fewer examples showcasing the contribution big data can make across many different areas of geography and planning.
{"title":"Introduction to Big Data Applications in Geography and Planning","authors":"M. Birkin, G. Clarke, J. Corcoran, R. Stimson","doi":"10.4337/9781789909791.00006","DOIUrl":"https://doi.org/10.4337/9781789909791.00006","url":null,"abstract":"This chapter introduces the book ‘Big Data Applications in Geography and Planning: An Essential Companion’, which showcases applications of big data in human geography and urban planning. First we provide, as editors, some background on our own experience of dealing with big data and applied spatial modelling through various large-scale, international Government intiatives in both the UK and Australia. Then we review the big debates on using big data by focusing on three core areas and arguing that the material in the chapters of this book will make a significant contribution to each of these issues. The first debate focuses on the nature of theory building and the analytical techniques needed to process and analyse big data. We note here that all the chapters in the book discuss spatial data analysis in a big data environment. Second, we discuss issues surrounding data quality, data cleaning and data being fit for purpose. The variety of data used in the applications in the book should be a source of some consolation here to those particularly concerned with representation. If one source is heavily skewed toward a particular activity or sub-group we argue that this can be compensated by another source which has different characteristics. This is demonstrated in many chapters of the book. This discussion also considers ethics and concerns around confidentiality. Finally, but importantly, we recognise that proponents of big data still need to win over many sceptics concerning the contribution that the new data can make to traditional social science problems. How can big data supplement or even replace traditional survey data in the future? Although the literature is awash with articles discussing issues around big data we argue that there are fewer examples showcasing the contribution big data can make across many different areas of geography and planning.","PeriodicalId":118028,"journal":{"name":"Big Data Applications in Geography and Planning","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129568899","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}
Pub Date : 1900-01-01DOI: 10.4337/9781789909791.00014
Nick Hood, G. Clarke, A. Newing, T. Rains
{"title":"Applications of store loyalty card big data in the location planning process","authors":"Nick Hood, G. Clarke, A. Newing, T. Rains","doi":"10.4337/9781789909791.00014","DOIUrl":"https://doi.org/10.4337/9781789909791.00014","url":null,"abstract":"","PeriodicalId":118028,"journal":{"name":"Big Data Applications in Geography and Planning","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127755930","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}
Pub Date : 1900-01-01DOI: 10.4337/9781789909791.00011
A. Comber, C. Brunsdon, M. Charlton, J. Cromby
{"title":"The changing geography of clinical misery in England: lessons in spatio-temporal data analysis","authors":"A. Comber, C. Brunsdon, M. Charlton, J. Cromby","doi":"10.4337/9781789909791.00011","DOIUrl":"https://doi.org/10.4337/9781789909791.00011","url":null,"abstract":"","PeriodicalId":118028,"journal":{"name":"Big Data Applications in Geography and Planning","volume":"35 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120931101","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}
Pub Date : 1900-01-01DOI: 10.4337/9781789909791.00008
N. Lomax
{"title":"Estimating household mobility using novel big data","authors":"N. Lomax","doi":"10.4337/9781789909791.00008","DOIUrl":"https://doi.org/10.4337/9781789909791.00008","url":null,"abstract":"","PeriodicalId":118028,"journal":{"name":"Big Data Applications in Geography and Planning","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130510309","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}
Pub Date : 1900-01-01DOI: 10.4337/9781789909791.00012
F. Pontin
{"title":"Utilising smartphone data to explore spatial influences on physical activity","authors":"F. Pontin","doi":"10.4337/9781789909791.00012","DOIUrl":"https://doi.org/10.4337/9781789909791.00012","url":null,"abstract":"","PeriodicalId":118028,"journal":{"name":"Big Data Applications in Geography and Planning","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122281222","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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