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.

Autorentext
Sandeep Narayan Kundu, Ph.D., is a Geoscientist with over 20 years of experience in the Geospatial Domain. He currently is a Spatial Geoscientist with Fugro Singapore Marine where is conducts marine survey and maps seabed hazards. Previously, he taught at the Department of Geography and the Department of Civil and Environmental Engineering at National University of Singapore, where he researched and taught subjects on Geographical Information Systems and Exploration Geoscience. Dr. Kundu is a British Council Fellow, and his interests lie in advancing geospatial technologies for Earth and Environmental modeling.

Inhalt

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