Welcome to GeoSocial 2023!

1st ACM SIGSPATIAL International Workshop on Geocomputational Analysis of Socio-Economic Data (GeoSocial 2023)

To be held in conjunction with 31st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2023)

In collaboration with ACM SIGSPATIAL 2023, we are pleased to announce the call for papers for GeoSocial 2023, a workshop dedicated to bridging the gap between geocomputational methods and socio-economic data analysis. The workshop aims to bring together a diverse community of researchers, practitioners, and students from various disciplines to exchange ideas, share knowledge, and foster collaboration in the burgeoning field of geocomputational analysis.

Call for Papers

We invite original research contributions and work-in-progress papers related to the workshop's theme. Topics of interest include but are not limited to:

  • AI/ML driven geospatial analysis of socio-economic data
  • Predictive models for socio-economic datasets - spatial regression models, deep learning models etc.
  • Spatial data infrastructure for harvesting, analysing of socio-economic indicators
  • Volunteered Geographic Information (VGI) for socio-economic data collection
  • Sustainable socio-economic development, Promoting sustainable socio-economic growth
  • Satellite remote sensing data, night-time light (NTL) data for socio-economic data analysis
  • Data fusion techniques and analytics for prediction and forecasting across space and time
  • Challenges of global change, climate change
  • Analysis of socio-demographic data (income) and integration of contextual datasets (night-time light, transportation, housing conditions, greenhouse gas emissions) which can act as a proxy of socio-economic indicator
  • Real-time analysis of dynamic and distributed socio-economic data
  • Geo-intelligence for sustainable socio-economic development

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome:

  • Full papers: Full papers (limited to 10 pages) should present original, unpublished research papers which belong to the scope of the workshop and are not being considered for publication in any other forum.
  • Short papers: Short papers (limited to 4 pages) may focus on a narrowly defined topic, may report on preliminary results (or work-in progress), or may present relevant study results that do not warrant a full paper. The short papers should be original, unpublished, within the scope of the workshop and are not being considered for publication in any other forum.
Accepted papers will be included in the workshop proceedings, which will be published in the ACM Digital Library. At least one author per accepted workshop contribution (full paper, short paper) is required to register for the workshop and the conference, as well as attend the workshop to present the accepted work. Otherwise, the accepted submission will not appear in the ACM Digital Library version of the workshop proceedings.

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at http://www.acm.org/publications/proceedings-template. Please check the ACM SigSpatial 2023 paper formatting guidelines for more information. We look forward to your contributions and particiaptions in our workshop.

Papers should be submitted electronically through the EasyChair submission system: Submission Link

Important Dates

Submission deadline: September 25, 2023 (AoE) - Final Extension
Notification of acceptance: October 10, 2023
Camera-ready papers due: October 13, 2023 (AoE)
Workshop date: Monday, November 13, 2023, 13:00-18:00

Detailed Program (Monday 13th November 2023, 13:00-18:00 hours)

Venue: Radisson Blu Hotel Hamburg (Congressplatz 2 D-20355 Hamburg)
[Room: Shanghai]

Best Paper Award:

Title: Wealth Index Estimation using Machine Learning with Environmental, Demographics, Remote Sensing, and Points of Interest Data
Authors: Dustin Reyes, Roger Jr. Antonio, Ardie Orden,Adrienne Heinrich, Randy Phoa, Sara Bilalx, Gerando Bonganay and Maria Singson

(All times in CET)


Opening remarks by GeoSocial'23 Workshop Organizers


Keynote talk by Dr. Stephan Winter

Professor, Spatial Information Science, Department of Infrastructure Engineering, The University of Melbourne, Australia

Title: Geo-Social

Abstract : I will take the liberty and expand the context of geocomputational analysis of socio-economic data, asking for social relevance and for social responsibility of geocomputational analysis. And of course, I will sprinkle this with some examples of my group's work, and thoughts on what I should have done, and hope to excite discussion.

About the Speaker : Stephan Winter is Professor in Spatial Information Science at the Department of Infrastructure Engineering, The University of Melbourne. He is specializing on human spatial cognition and communication, with a vision of developing intelligent spatial machines especially in the context of indoor and outdoor spatial modelling, wayfinding, navigation, and intelligent transport. Increasingly this includes sustainable mobility, and walking as an active mode of mobility.


Paper Presentation: 20min, QA: 5min

Title: Measurement of spatial inequality using micro-spatial data in Thailand

Authors: Hiroki Baba. Hitotsubashi University, Japan.

Summary: 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 inequality. The results of this study were that a certain percentage of local residents had difficulty accessing urban facilities, especially public transport hubs, even when using automobiles. Moreover, in some Mueang units, access to public transport hubs is generally poor due to an aging population. Although the elderly and low-income groups need to fulfill their needs within a walking distance, this study demonstrates this is difficult for them. Focusing on spatial inequalities in case of grocery stores, not only Mueang units in rural areas, but also ones in Bangkok metropolitan region experience spatial inequality. Despite technical and data limitations, these findings are essential for mitigating spatial inequality.


Paper Presentation: 20min, QA: 5min

Title: Assessing the relationship between socio-demographic characteristics and OpenStreetMap contributor behaviours

Authors: Dominick Sutton, Guy Solomon, Xinyi Yuan, Merve Polat Kayali, Zoe Gardner and Ana Basiri. University of Glasgow, UK.

Summary: 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 under-representation of particular socio-demographic groups, which may lead to biases in the types of information contributed. This paper examines the relationship between demographic characteristics and user contributions to OpenStreetMap (OSM), one of the most successful examples of a project reliant on VGI. It demonstrates statistically significant differences in the information provided by users of different genders, ages, and education-levels. Differences between the demographic characteristics of OSM contributors and the underlying population are therefore likely to be reflected in the VGI contained in OSM.


Tea Break


Paper Presentation: 20min, QA: 5min

Title: Wealth Index Estimation using Machine Learning with Environmental, Demographics, Remote Sensing, and Points of Interest Data

Authors: Dustin Reyes*, Roger Jr. Antonio*, Ardie Orden*,Adrienne Heinrich*, Randy Phoa#, Sara Bilalx, Gerando Bonganay+ and Maria Singson#. Aboitiz Data Innovation, Philippines*, IBM, Singapore#, IBM New Zealandx, IBM Philippines+.

Summary: The UN aims for poverty alleviation, especially in developing countries, but traditional wealth-measuring surveys are costly and infrequent. This study used machine learning with tabular data to estimate wealth in the Philippines, achieving better accuracy than previous deep learning models with satellite imagery. Enhanced with environmental and demographic data, their model excelled particularly in urban areas. The research suggests that automatically collected environmental data can replace manual data collection, with future studies recommended to focus on urban-rural differences and incorporating deep learning where data is limited.


Paper Presentation: 20min, QA: 5min

Title: Spatial Optimization Site Selection of Beijing Cainiao Station Based on Multi-Source Geospatial Data

Authors: Shaohua Wang*, Zezhi Zhang#, Cheng Su*, Liang Zhou#, Haojian Liang+ and Wenda Wang#. International Research Center of Big Data for Sustainable Development Goals, China*, Lanzhou jiaotong university, China#, Jilin university, China+.

Summary: The study examines the optimal locations for express delivery self-pickup points in Beijing using various datasets including Point Of Interest (POI) and population distribution. It found that the distribution of Cainiao Stations in Beijing is influenced by land prices, administrative zones, and population density, with most stations concentrated in densely populated areas and fewer in peripheral districts with fewer residents. The research also revealed that the proximity of residential areas, vegetable stores, universities, and office buildings significantly affects the location of these stations.


Paper Presentation: 20min, QA: 5min

Title: A hybrid model for Forecasting Biological Oxygen Demand using CEEMDAN-LSTM

Authors: Neha Pant, Durga Toshniwal and Bhola Gurjar. Indian Institute of Technology Roorkee, India.

Summary: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 approach incorporating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Long Short-Term Memory for forecasting Biological Oxygen Demand(BOD). The approach is minimalistic that solely utilizes historical data. The CEEMDAN decomposition is applied to the original time series data to generate a set of Intrinsic Mode Functions(IMFs) with varied frequencies and a residual, thus capturing the non-linear and non-stationary characteristics of the data. LSTM is then employed to forecast the IMFs and residuals produced by CEEMDAN. Finally, all the forecasted IMFs and residuals are aggregated to generate the final forecast. To conduct a thorough and rigorous analysis, the CEEMDAN-LSTM model is used to forecast BOD levels at three monitoring stations flowing along the river Ganga in Kanpur district of the state of Uttar Pradesh, India, considering one, two, and three-hour forecasting horizons. Experimental results demonstrate that using CEEMDAN combined with LSTM can effectively detect the complex non-linear patterns in the time series data, leading to more accurate outcomes than the alternative techniques.


Paper Presentation: 20min, QA: 5min

Title: Exploring the Relationship between Greenery in Patients’ Living Spaces and Cognitive health

Authors: Yoohyung Joo*, Sangyoon Park*, Jaeyoung Jung*, Jiwan Hong*, Juyeon Ko#, Jaelim Cho#, Changsoo Kim# and Joon Heo*. Yonsei University, South Korea*, and Yonsei University College of Medicine, South Korea#.

Summary: The study explored the link between greenery and dementia risk, considering both overall greenery and open greenery within 500 meters of patients' homes using street view imagery and traditional satellite images. While increased greenery generally correlated with a reduced dementia risk, higher levels of open greenery and satellite-derived EVI indicated an elevated risk. This association fluctuated based on geographic locations: in rural areas, there was a consistent relationship between greenery and reduced dementia risk, but in urban areas, notably Seoul, the findings were more variable, with only specific regions showing reduced dementia risks linked to higher greenery levels.


Closing Remarks by GeoSocial'23 Workshop Organizers

Workshop Organizers

Organizer 1

Soumya K. Ghosh

Professor, Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India

Organizer 4

Shreya Ghosh

Postdoctoral Scholar
The Pennsylvania State University, USA

Organizer 2

Budhendra Bhaduri

Director, Geospatial Science and Human Security Division, Oak Ridge National Laboratory

Organizer 4

Alexander Zipf

Professor, Heidelberg University, Chair of GIScience, HeiGIT Heidelberg Institute for Geoinformation Technology


  • Wenwen Li, Arizona State University, USA
  • Rene Westerholt, TU Dortmund University, Dortmund, Germany
  • Paul Longley, University College London, London, UK
  • Pabitra Mitra, Indian Institute of Technology Kharagpur, India
  • Anupam Khan, Indian Institute of Technology Kharagpur, India
  • Edwin Sabuhoro, Pennsylvania State University, USA
  • Sayak Roychowdhury, Indian Institute of Technology Kharagpur, India
  • Rajan K.S., International Institute of Information Technology, Hyderabad, India
  • Jamal Jokar Arsanjani, Alborg University, Denmark
  • Bharath H Aithal, Indian Institute of Technology Kharagpur, India
  • Sven Lautenbach, Heidelberg University, Germany
  • Stefano De Sabbata, The University of Leicester, Leicester, UK
  • Jaydeep Das, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India