Social Sensing and Urban Flooding: Socio-Spatial Insights from Citizen-Generated Data in Makassar City, Indonesia

Rusdi Rusdi; Helmi Ayuradi Miharja

doi:10.37905/jgej.v7i1.35890

Social Sensing and Urban Flooding: Socio-Spatial Insights from Citizen-Generated Data in Makassar City, Indonesia

Rusdi Rusdi, Helmi Ayuradi Miharja

Abstract

Urban flooding in rapidly urbanizing coastal cities increasingly exceeds the capacity of conventional monitoring and response systems to capture its localized, time-sensitive, and socially differentiated impacts. This study examines the potential of social sensing as a complementary approach for understanding urban flooding as a socio-spatial process shaped by everyday experiences, digital participation, and governance practices, with an empirical focus on Makassar City, Indonesia. Drawing on citizen-generated Instagram content collected between January 2019 and March 2024 (retained posts: n = [N]; geotagged posts used for spatial analysis: [P%]), the research integrates spatial, temporal, and qualitative signals derived from geotagged locations, posting timestamps, and visual–narrative materials to analyze flood dynamics. Social sensing outputs are triangulated with institutional flood information (incident logs, response records, and hazard layers) to assess correspondence, gaps, and governance relevance. The findings indicate that social sensing captures impact-oriented flood information in locations where inundation disrupts everyday urban activities, provides early temporal signals associated with flood onset and escalation, and reveals qualitative dimensions of lived flood experience that are not represented in hydrological or administrative data alone. While spatial and temporal patterns broadly align with institutional records, citizen-generated reports can precede formal documentation and highlight highly localized effects (e.g., temporary road closures or neighborhood-scale ponding) that remain underreported in official datasets. Methodologically, the study advances a human-centered analytical framework that bridges digital geographic analysis with qualitative interpretation, prioritizing transparency, interpretability, and ethical handling of publicly available social media data (e.g., de-identification and quotation minimization). From a governance perspective, the results demonstrate the value of integrating social sensing into hybrid urban flood governance to support more adaptive, context-sensitive, and inclusive approaches to flood risk management.

Keywords

coastal cities digital geographic data; lived risk experience; social sensing; urban flood governance

Full Text:

PDF

References

Arthur, R., Boulton, C. A. B., Shotton, H., & Williams, H. T. P. (2018). Social sensing of floods in the UK. PLOS ONE, 13(1), e0189327. https://doi.org/10.1371/journal.pone.0189327

Barker, J. L. P., & Macleod, C. J. A. (2019). Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities. Environmental Modelling & Software, 115, 213–227. https://doi.org/10.1016/j.envsoft.2018.11.013

Brown, G., & Kyttä, M. (2014). Key issues and research priorities for public participation GIS (PPGIS): A synthesis based on empirical research. Applied Geography, 46, 122–136. https://doi.org/10.1016/j.apgeog.2013.11.004

Cash, D. W., Clark, W. C., Alcock, F., Dickson, N. M., Eckley, N., Guston, D. H., Jäger, J., & Mitchell, R. B. (2003). Knowledge systems for sustainable development. Proceedings of the National Academy of Sciences, 100(14), 8086–8091. https://doi.org/10.1073/pnas.1231332100

Cresswell, T. (2015). Place: An introduction (2nd ed.). Wiley Blackwell. https://www.wiley.com/en-us/Place%3A%2BAn%2BIntroduction%2C%2B2nd%2BEdition-p-9780470655627

Crooks, A., Croitoru, A., Stefanidis, A., & Radzikowski, J. (2013). Earthquake: Twitter as a Distributed Sensor System. Transactions in GIS, 17(1), 124–147. https://doi.org/10.1111/j.1467-9671.2012.01359.x

Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242–261. https://doi.org/10.1111/1540-6237.8402002

de Albuquerque, J. P., Herfort, B., Brenning, A., & Zipf, A. (2015). A geographic approach for combining social media and authoritative data towards identifying useful information for disaster management. International Journal of Geographical Information Science, 29(4), 667–689. https://doi.org/10.1080/13658816.2014.996567

de Bruijn, J. A., de Moel, H., Jongman, B., de Ruiter, M. C., Wagemaker, J., & Aerts, J. C. J. H. (2019). A global database of historic and real-time flood events based on social media. Scientific Data, 6, 311. https://doi.org/10.1038/s41597-019-0326-9

Di Baldassarre, G., Kooy, M., Kemerink, J. S., & Brandimarte, L. (2013). Towards understanding the dynamic behaviour of floodplains as human-water systems. Hydrology and Earth System Sciences, 17(8), 3235–3244. https://doi.org/10.5194/hess-17-3235-2013

Douglas, I., Alam, K., Maghenda, M., McDonnell, Y., McLean, L., & Campbell, J. (2008). Unjust waters: Climate change, flooding and the urban poor in Africa. Environment and Urbanization, 20(1), 187–205. https://doi.org/10.1177/0956247808089156

D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press. https://doi.org/10.7551/mitpress/11805.001.0001

Elwood, S., Goodchild, M. F., & Sui, D. Z. (2012). Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice. Annals of the Association of American Geographers, 102(3), 571–590. https://doi.org/10.1080/00045608.2011.595657

Fohringer, J., Dransch, D., Kreibich, H., & Schröter, K. (2015). Social Media as an Information Source for Rapid Flood Mapping. Natural Hazards and Earth System Sciences, 15(12), 2725–2738. https://doi.org/10.5194/nhess-15-2725-2015

Goodchild, M. F. (2007). Citizens as Sensors: The World of Volunteered Geography. GeoJournal, 69(4), 211–221. https://doi.org/10.1007/s10708-007-9111-y

Hallegatte, S., Vogt-Schilb, A., Bangalore, M., & Rozenberg, J. (2017). Unbreakable: Building the resilience of the poor in the face of natural disasters. World Bank. https://doi.org/10.1596/978-1-4648-1003-9

Harvey, D. (1996). Justice, nature and the geography of difference. Blackwell Publishers. https://www.wiley.com/en-us/Justice%2C%2BNature%2Band%2Bthe%2BGeography%2Bof%2BDifference-p-9781557866813

Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015). Processing social media messages in mass emergency: A survey. ACM Computing Surveys, 47(4), Article 67. https://doi.org/10.1145/2771588

Intergovernmental Panel on Climate Change. (2023). Climate Change 2023: Synthesis Report. https://www.ipcc.ch/report/ar6/syr/

Jasanoff, S. (Ed.). (2004). States of knowledge: The co-production of science and social order. Routledge. https://www.routledge.com/States-of-Knowledge-The-Co-production-of-Science-and-the-Social-Order/Jasanoff/p/book/9780415403290

Jha, A. K., Bloch, R., & Lamond, J. (2012). Cities and flooding: A guide to integrated urban flood risk management for the 21st century. The World Bank. https://doi.org/10.1596/978-0-8213-8866-2

Kar, B., Sieber, R., Haklay, M., & Ghose, R. (2016). Public Participation GIS and Participatory GIS in the Era of GeoWeb. The Cartographic Journal, 53(4), 296–299. https://doi.org/10.1080/00087041.2016.1256963

Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Van Hentenryck, P., Fowler, J., & Cebrian, M. (2016). Rapid assessment of disaster damage using social media activity. Science Advances, 2(3), e1500779. https://doi.org/10.1126/sciadv.1500779

Meadow, A. M., Ferguson, D. B., Guido, Z., Horangic, A., Owen, G., & Wall, T. (2015). Moving toward the deliberate coproduction of climate science knowledge. Weather, Climate, and Society, 7(2), 179–191. https://doi.org/10.1175/WCAS-D-14-00050.1

Morss, R. E., Demuth, J. L., Lazrus, H., et al. (2017). Hazardous weather prediction and communication in the modern information environment. Bulletin of the American Meteorological Society, 98(12), 2653–2674. https://doi.org/10.1175/BAMS-D-16-0058.1

O’Donnell, E. C. (2020). Drivers of future urban flood risk. Philos Trans A Math Phys Eng Sci, 378(2168), 20190216. https://doi.org/10.1098/rsta.2019.0216

Resch, B., Usländer, F., & Havas, C. R. (2018). Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment. Cartography and Geographic Information Science, 45(4), 362–376. https://doi.org/10.1080/15230406.2017.1356242

Satterthwaite, D. (2013). The Political Underpinnings of Cities’ Accumulated Resilience to Climate Change. Environment and Urbanization, 25(2), 381–391. https://doi.org/10.1177/0956247813500902

Shelton, T., Poorthuis, A., & Zook, M. (2015). Social Media and the City: Rethinking Urban Socio-spatial Inequality Using User-generated Geographic Information. Landscape and Urban Planning, 142, 198–211. https://doi.org/10.1016/j.landurbplan.2015.02.020

Sieber, R. (2006). Public Participation Geographic Information Systems: A Literature Review and Framework. Annals of the Association of American Geographers, 96(3), 491–507. https://doi.org/10.1111/j.1467-8306.2006.00702.x

Taylor, L. (2017). What is data justice? The case for connecting digital rights and freedoms globally. Big Data & Society, 4(2), 1–14. https://doi.org/10.1177/2053951717736335

Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S., Brakenridge, G. R., Erickson, T. A., & Slayback, D. A. (2021). Satellite imaging reveals increased proportion of population exposed to floods. Nature, 596(7870), 80–86. https://doi.org/10.1038/s41586-021-03695-w

Tuan, Y.-F. (1977). Space and place: The perspective of experience. University of Minnesota Press. https://www.upress.umn.edu/9780816638772/space-and-place/

Vieweg, S., Hughes, A. L., Starbird, K., & Palen, L. (2010). Microblogging during two natural hazards events: What Twitter may contribute to situational awareness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’10) (pp. 1079–1088). Association for Computing Machinery. https://doi.org/10.1145/1753326.1753486

DOI: https://doi.org/10.37905/jgej.v7i1.35890