With the advancement in computing capabilities and data collection technologies, a huge volume of socio-economic data is being harvested. This vast data, inherently spatial, demands sophisticated computational methods for effective analysis, extraction of meaningful insights, and addressing critical socio-economic issues at both micro and macro levels. Despite the potential, the integration of geocomputational methods and socio-economic data analysis is still in its infancy. The GeoSocial-2023 workshop will aim to bridge this gap by providing a platform for knowledge exchange and collaboration. Through the exchange of cutting-edge research findings, technical skills, and interdisciplinary discussions, the workshop intends to advance the field of geocomputation and its application to socio-economic data. The ultimate goal is to leverage geocomputational methods to better understand, predict, and address socio-economic issues on both local and global scales.
Proceeding Downloads
Measurement of spatial inequality using micro-spatial data in Thailand
This study aims to discuss the spatial heterogeneity of accessibility of urban facilities and analyze the differences in socioeconomic characteristics in Thailand using the following indicators: walking and car accessibility, and intra-spatial ...
Assessing the relationship between socio-demographic characteristics and OpenStreetMap contributor behaviours
'Volunteered Geographic Information' (VGI) has particular importance - in part - for its democratisation of geographic information. However, some recent research has suggested that despite being publicly open, several successful VGI platforms have ...
Wealth Index Estimation using Machine Learning with Environmental, Demographics, Remote Sensing, and Points of Interest Data
- Dustin Reyes,
- Roger Jr. Antonio,
- Ardie Orden,
- Adrienne Heinrich,
- Randy Phoa,
- Sara Bilal,
- Gerando Bonganay,
- Maria Singson
Poverty alleviation is an important goal set by the UN which aims to improve the lives of a large number of people around the world, especially in developing countries. One of the challenges in achieving this goal is being able to measure wealth level ...
Spatial Optimization Site Selection of Beijing Cainiao Station Based on Multi-Source Geospatial Data
As express delivery self-pickup becomes a part of people's daily lives, the location optimization of express delivery self-pickup points has become important research content. This study is based on the POI data of Beijing Cainiao Station and postal ...
A hybrid model for Forecasting Biological Oxygen Demand using CEEMDAN-LSTM
Reliable and accurate forecasting of water quality parameters is essential for water quality management. Existing methods often rely on external factors and multiple water quality parameters. In this study, we demonstrate the applicability of a hybrid ...
Exploring the Relationship between Greenery in Patients’ Living Spaces and Cognitive Health: A Study of Urban versus Rural Areas
This research examined the relationship between greenery and the risk of dementia. When analyzing the association with the risk of dementia, we considered both greenery and open greenery(greenery with a clear view of the sky) within 500 meters of ...
Index Terms
- Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geocomputational Analysis of Socio-Economic Data