Mapping socioeconomic status and trend: toward integrated remote and social sensing applications

Type: Virtual Poster
Theme:
Sponsor Groups:
Poster #:
Day: 4/9/2021
Start / End Time: 8:00 AM / 9:15 AM (PST)
Room: Virtual 26
Organizers: Oleksandr Karasov, Evelyn Uuemaa, Tiit Tammaru
Chairs:

Call for Submissions

This session seeks for participants, who work on any aspect of socioeconomic status assessment of neighborhoods: either from space, ground, or combined. The session gathers the professionals from different disciplines to embrace and mutually enrich the existing mapping methodologies for various socioeconomic aspects of urban space. We especially welcome the submissions, discussing the quality and robustness of socioeconomic classifications and mappings, ground-truth validation issues (including validation based on traditional data from censuses and registers), applications of machine learning to capture information from both satellite and ground-based images. In this way, through this session, we will find the methodological pathways to increase the clarity and quality of the socioeconomic models and predictions based on complementary data sources.


Description

Remotely sensed (primarily satellite imagery) and ground-based (Google Street View panoramas, social media posts, mobile phone calls) data have become the major proxies on the various aspects of socioeconomic status of the neighborhoods. These aspects include but are not limited to: slum versus non-slum areas mapping, income and education levels, social connections and deprivation, accessibility to the green and blue spaces, typology of economic activities, demographic characteristics. The remote sensing-based studies benefit from the very high-resolution imagery, accounting for the spectral and textural features of environment: building types, the availability of green and blue spaces, vertical structure of neighborhoods, etc. The ground-based data, coming from Google Street View street-level imagery, geo-tagged social media photographs or own authors' observations, metadata of phone calls, provide another kind of insights: population density, visitation patterns, professional and income status of social media users, neighborhood characteristics, amount of perceived greenery, the diversity of activities and venues. While these two major directions of research—remotely sensed and ground-based—on socioeconomic status have been developed independently, they are to a large extent complementary and gradually the body of research focusing on their integration is growing. At the same time, there are many associated uncertainties, inherent in the remote sensing and ground-based images: trade-offs between the spatiotemporal resolution and coverage, limited performance of existing spectral and textural metrics, social media data biases and availability, local environmental and cultural specifics, etc., which are of particular interest in this session.


Agenda

ID Title Participant
001 Modelling Socio-Economic Conditions in Sub-Saharan African Cities Monika Kuffer
University of Twente, FACULTY ITC
002 Access to Urban Greenspace: a preliminary analysis of the Hispanic population’s accessibility to urban greenspace in Gainesville, Georgia Catlin Corrales
003 Classifying swimming pool using remote sensing and LIDAR in Staten Island, New York Robert Abugel
Hunter College - City University
004 Baseline Community Data Jamal Rollins
Self-employed
005 Understanding urban sprawl in a West African metropolis: A case study of Abidjan city in Ivory Coast Gerard Allali
006 Neighbourhood types relate to income levels in subdistricts of Tallinn, Estonia Oleksandr Karasov
University of Tartu
007 Mapping the Vulnerabilities of Green Stormwater Infrastructure Planning in Philadelphia Kate Homet
Villanova University

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