Geospatial data analytics and urban applications
Author(s)
Bibliographic Information
Geospatial data analytics and urban applications
(Advances in 21st century human settlements)
Springer, c2022
Available at 2 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references
Description and Table of Contents
Description
This book highlights advanced applications of geospatial data analytics to address real-world issues in urban society. With a connected world, we are generating spatial at unprecedented rates which can be harnessed for insightful analytics which define the way we analyze past events and define the future directions. This book is an anthology of applications of spatial data and analytics performed on them for gaining insights which can be used for problem solving in an urban setting. Each chapter is contributed by spatially aware data scientists in the making who present spatial perspectives drawn on spatial big data. The book shall benefit mature researchers and student alike to discourse a variety of urban applications which display the use of machine learning algorithms on spatial big data for real-world problem solving.
Table of Contents
1. Geospatial Big Data and Urban Applications
2. Spatial Proximity to Amenities and Home search
3. Spatial patterns of Air Quality and Urban Heat Island effects during COVID-19
4. Population Dynamics during Pandemic Lockdown and beyond
5. Geospatially reasoning the shooting incidents of New York City
6. Activity Patterns from Twitter Data - A Singapore Case Study
7. Spatio-temporal analysis of Tourist Source Market Emissiveness in Shanghai
8. A Spatial Perspective on Crime patterns in Chicago
9. Private-hire cars distribution in space and time - A study on Grab Cars
by "Nielsen BookData"