Spatial analysis using big data : methods and urban applications
Contributor(s): Yamagata, Yoshiki [ed.] | Seya. Hajime [ed.].
Series: Spatial econometrics and spatial statistics. Publisher: London Academic Press 2020Description: xvii, 284p.ISBN: 9780128131275.Subject(s): Spatial analysis (Statistics) -- Data processing | Urban geography -- Data processingDDC classification: 307.76028557 | Sp28 Summary: Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems. The methods represent a cluster of potentially transformational socio-economic modeling tools that allow researchers to capture real-time and high-resolution information to potentially reveal new socioeconomic dynamics within urban populations. Each method, written by leading exponents of the discipline, uses real-time urban big data to solve research problems in spatial science. Urban applications of these methods are provided in unsurpassed depth, with chapters on surface temperature mapping, view value analysis, community clustering and spatial-social networks, among many others. Reviews some of the most powerful and challenging modern methods to study big data problems in spatial science Provides computer codes written in R, MATLAB and Python to help implement methods Applies these methods to common problems observed in urban and regional economics| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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PK Kelkar Library, IIT Kanpur | General Stacks | 307.76028557 Sp28 (Browse shelf) | Available | A185190 |
Spatial Analysis Using Big Data: Methods and Urban Applications helps readers understand the most powerful, state-of-the-art spatial econometric methods, focusing particularly on urban research problems. The methods represent a cluster of potentially transformational socio-economic modeling tools that allow researchers to capture real-time and high-resolution information to potentially reveal new socioeconomic dynamics within urban populations. Each method, written by leading exponents of the discipline, uses real-time urban big data to solve research problems in spatial science. Urban applications of these methods are provided in unsurpassed depth, with chapters on surface temperature mapping, view value analysis, community clustering and spatial-social networks, among many others.
Reviews some of the most powerful and challenging modern methods to study big data problems in spatial science
Provides computer codes written in R, MATLAB and Python to help implement methods
Applies these methods to common problems observed in urban and regional economics
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