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Using OpenStreetMap Building Footprints Data for Population Distribution Model: A Case Study in Cavite, Philippines

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A lightning talk delivered by Feye Andal at State of the Map 2019 in Heidelberg, Germany

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Using OpenStreetMap Building Footprints Data for Population Distribution Model: A Case Study in Cavite, Philippines

  1. 1. Using OpenStreetMap Building Footprints Data for Population Distribution Model: A Case Study in Cavite, Philippines State of the Map 2019 Heidelberg, Germany Scholar Lightning Talk Sept. 22, 2019
  2. 2. HELLO! I am Feye Andal GIS Specialist/Researcher, UP Resilience Institute Volunteer, OpenStreetMap Philippines @feyeandal 2
  3. 3. RATIONALE ▪ Philippines has undergone rapid urbanization and population growth but existing spatial population distribution data is still lacking ▪ Spatially accurate population distribution maps are essential for disaster risk assessment (from awareness to response) 3
  4. 4. 4
  5. 5. Volunteered Geographic Information (VGI) A geographic information collected and shared voluntarily by the general public (e.g. OpenStreetMap) 5
  6. 6. METHODS 6 extract OSM buildings (Geofabrik) digitize and validate OSM building footprints data (JOSM) run Population Distribution Model (arcpy) download and process 2015 Census of Population (PSA)
  7. 7. OUR PROCESS IS SIMPLE generate 10m grids of brgy boundary join population field of brgys and brgy boundary then spatial join frequency analysis 7 intersect 10m grids to OSM buildings spatial join urban grids with brgy boundaries join fields of brgy boundary and point counts add field to brgy boundary calculate field (popn2015/ frequency) convert polygon to raster project raster (optional)
  8. 8. 8 digitizing and validating OSM building footprints via JOSM
  9. 9. 9 OSM Building Footprints (Jan 2019)
  10. 10. 10 generated Population RasterRESULTS: Population Count 1-22 23-45 46-71 71-100 101-147 148-231 231-464 Municipal Boundary
  11. 11. FINDINGS ▪ The results showed that OSM database can be used to produce population distribution map at detail scale ▪ Although OSM is useful for generating population map, it has many weaknesses such as misidentification of buildings, and topology of building footprints, that is why validation of OSM data is important 11
  12. 12. CHALLENGES ▪ Achieving high completeness , consistency, and accuracy of OSM data is time- consuming and labor-intensive 12
  13. 13. CHALLENGES ▪ The run time of the population distribution model is long (average run time is ~4hrs) 13
  14. 14. 14 Sample Analysis of Population Map from OSM
  15. 15. 15 100-Year Flood Hazard (UP NOAH) Low Moderate High Municipal Boundary
  16. 16. 16 Overlay Analysis of Flood Hazard and Population Flood Hazard Low Moderate High Population Count 1-22 23-45 46-71 71-100 101-147 148-231 231-464 Municipal Boundary
  17. 17. 3,678,301total population in Cavite as of 2015 17
  18. 18. 1,684,735 low (<0.5m) 527,102 high (>1.5m) 1,387,293 moderate (0.5-1.5m) 18 POPULATION EXPOSED TO 100-YEAR FLOOD
  19. 19. 1,480,876total population not exposed to 100-year flood hazard 19
  20. 20. THANKS! Any questions? You can find me at: @feyeandal dhandal@up.edu.ph 20
  21. 21. REFERENCES: ▪ Rizqihandari, N. and Indratmoko, S. (2016). Using OpenStreetMap Data for Population Model. Advances in Social Sciences, Education and Humaities Research, Vol 79 ▪ Bonafilia, et. al (2019) Mapping for humanitarian aid and development with weakly and semi-supervised learning. https://ai.facebook.com/blog/mapping-the-world-to-help- aid-workers-with-weakly-semi-supervised-learning/ 21

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