EFFECT ASSESSMENT OF LARGE-SCALE EVENTS VIA SPATIOTEMPORAL APPROACH
The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences
; XLVI-3/W1-2022:15-20, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1811068
ABSTRACT
Together with rapid development of location-based services and big-data platforms especially in urban areas, huge amount of spatiotemporal data are collected without properly used;on the other hand, state-of-the-art quantitative policy effect assessment techniques usually require panel data as input. To solve both issues, this paper follows the following approach:
obtaining panel data by aggregating spatiotemporal data and feeding them to the effect assessment module. With the help of high-performance computing techniques which are able to deal with huge amount of data, we build framework Aggr-analysis which applies clustering algorithms to shrink the raw data set and find associations between different data sets via co-location analysis. Finally, we prove the effectiveness by an example analysis of resident activities during the COVID-19 Pandemic. We apply Aggr-analysis to process the share-bike usage data and POI (Point Of Interest) data in Beijing, then obtain the panel data required by DID (Difference-in-Differences) method. Supplemented with environmental data, we conclude the net effect of the COVID-19 breakout on society and economy - the pandemic has reduced the overall resident mobility by 64.8% within two months.
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Experimental Studies
Language:
English
Journal:
The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences
Year:
2022
Document Type:
Article
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