What is it about?
The current research observing and analyzing urban activities behavior was often supported by the volunteered sharing of geolocation and the activity performed in space and time.
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Why is it important?
The objective of this research was to observe the spatiotemporal and directional trends and the distribution differences of urban activities at the city and district levels using LBSN data. The density was estimated, and the spatiotemporal trend of activities was observed, using kernel density estimation (KDE); for spatial regression analysis, geographically weighted regression (GWR) analysis was used to observe the relationship between different activities in the study area. Finally, for the directional analysis, to observe the principle orientation and direction, and the spatiotemporal movement and extension trends, a standard deviational ellipse (SDE) analysis was used.
Perspectives
LBSN can be considered as a supplementary and reliable source of social media big data for observing urban activities and behavior within a city in space and time.
Dr. Muhammad Rizwan
Shanghai University
Read the Original
This page is a summary of: Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN, ISPRS International Journal of Geo-Information, February 2020, MDPI AG,
DOI: 10.3390/ijgi9020137.
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