Vol. 14 No. 4 (2023):
Research Article

PoD: A Web Tool for Population Downscaling Using Areal Interpolation and Volunteered Geographic Information

Marios Batsaris
Department of Geography, University of the Aegean, 81100 Mytilene, Greece
Sofia Zafeirelli
Department of Geography, University of the Aegean, 81100 Mytilene, Greece
Michail Vaitis
Department of Geography, University of the Aegean, 81100 Mytilene, Greece
Dimitris Kavroudakis
Department of Geography, University of the Aegean, 81100 Mytilene, Greece

Published 2023-10-25

Keywords

  • population downscaling,
  • areal interpolation,
  • web tool,
  • GIS

How to Cite

Batsaris, Marios, Sofia Zafeirelli, Michail Vaitis, and Dimitris Kavroudakis. 2023. “PoD: A Web Tool for Population Downscaling Using Areal Interpolation and Volunteered Geographic Information”. European Journal of Geography 14 (4):22-36. https://doi.org/10.48088/ejg.m.bat.14.4.022.036.
Received 2023-06-02
Accepted 2023-10-25
Published 2023-10-25

Abstract

Population data are commonly sourced from censuses, and to meet confidentiality requirements, they are spatially aggregated into standardized enumeration units. However, the need often arises to transform such datasets into user-defined spatial scales, a process known as areal interpolation. Areal interpolation is the process of data transformation across spatial zones and is particularly suitable for aggregated data such as census data. While numerous areal interpolation methods exist, a lack of implementation tools have been witnessed. In this article, we introduce PoD, a web-based solution that encompasses four downscaling schemes. To illustrate the utility of the proposed tool, we conducted a case study using actual data from the city of Mytilini, Greece. We compared the results obtained through PoD with existing R-based implementations, in addition to evaluating their performance using a reference dataset. The outcomes of this evaluation affirm the effectivenes of the proposed PoD tool over alternative implementations.

Highlights:

  • Areal interpolation is broadly used to facilitate the conversion of population data across spatial zones.
  • A notable aspect concerning the areal interpolation of population data pertains to the identified lack of implementation tools.
  • The proposed tool demonstrates higher performance compared to existing alternatives.

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References

  1. Bakillah, M., Liang, S., Mobasheri, A., Jokar Arsanjani, J., & Zipf, A. (2014). Fine-resolution population mapping using OpenStreetMap points-of-interest. International Journal of Geographical Information Science, 28(9), 1940–1963. https://doi.org/10.1080/13658816.2014.909045
  2. Bao, W., Gong, A., Zhang, T., Zhao, Y., Li, B., & Chen, S. (2023). Mapping Population Distribution with High Spatiotemporal Resolution in Beijing Using Baidu Heat Map Data. Remote Sensing, 15(2), 1–22. https://doi.org/10.3390/rs15020458
  3. Batsaris, M. (2021). populR: Population Down-Scaling in R. CRAN. https://cran.r-project.org/web/packages/populR/
  4. Batsaris, M., & Kavroudakis, D. (2021). populR: an R Package for Population Downscaling. The R Journal, 14(December), 223–234. https://doi.org/https://doi.org/10.32614/RJ-2023-007
  5. Batsaris, M., Kavroudakis, D., Soulakellis, N. A., & Kontos, T. (2019). Location-Allocation Modeling for Emergency Evacuation Planning in a Smart City Context. International Journal of Applied Geospatial Research, 10(4), 28–43. https://doi.org/10.4018/ijagr.2019100103
  6. Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., & Toivonen, T. (2022). A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data, 9(1), 1–19. https://doi.org/10.1038/s41597-021-01113-4
  7. Bertolotto, M., McArdle, G., & Schoen-Phelan, B. (2020). Volunteered and crowdsourced geographic information: The openstreetmap project. Journal of Spatial Information Science, 20(20), 65–70. https://doi.org/10.5311/JOSIS.2020.20.659
  8. Calka, B., Nowak Da Costa, J., & Bielecka, E. (2017). Fine scale population density data and its application in risk assessment. Geomatics, Natural Hazards and Risk, 8(2), 1440–1455. https://doi.org/10.1080/19475705.2017.1345792
  9. Cheng, J., Karambelkar, B., Xie, Y., Wickham, H., Russell, K., Schloerke, B., Agafonkin, V., Copeland, B., Dietrich, J., Besquet, B., AS, N., Voogdt, L., Montague, D., AB, K., Kajic, R., Bostock, M., Contributors, jQuery F. and, Contributors, L., CloudMade, … RStudioTeam. (2019). leaflet. CRAN. https://cran.r-project.org/web/packages/leaflet/leaflet.pdf
  10. Comber, A., & Zeng, W. (2019). Spatial interpolation using areal features: A review of methods and opportunities using new forms of data with coded illustrations. Geography Compass, 13(10), 1–23. https://doi.org/10.1111/gec3.12465
  11. Eicher, C. L., & Brewer, C. A. (2001). Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation. Cartography and Geographic Information Science, 28(2), 125–138. https://doi.org/10.1559/152304001782173727
  12. Fisher, P. F., & Langford, M. (1995). Modelling the errors in areal interpolation between zonal systems by Monte Carlo simulation. Environment & Planning A, 27(2), 211–224. https://doi.org/10.1068/a270211
  13. Freire, S., & Aubrecht, C. (2012). Integrating population dynamics into mapping human exposure to seismic hazard. Natural Hazards and Earth System Science, 12(11), 3533–3543. https://doi.org/10.5194/nhess-12-3533-2012
  14. Gervasoni, L., Fenet, S., Perrier, R., & Sturm, P. (2019). Convolutional neural networks for disaggregated population mapping using open data. Proceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018, 594–603. https://doi.org/10.1109/DSAA.2018.00076
  15. 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
  16. Goodchild, M. F., & Siu-Ngan Lam, N. (1980). Areal interpolation: a variant of the traditional spatial problem. Geo-Processing, 1(3), 297–312.
  17. Guo, H., Cao, K., & Wang, P. (2017). Population estimation in Singapore based on remote sensing and open data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 42(2W7), 1181–1187. https://doi.org/10.5194/isprs-archives-XLII-2-W7-1181-2017
  18. Halder, J. C. (2018). Population change and land use dynamics: A case study of Paschim Medinipur District, West Bengal, India. European Journal of Geography, 9(3), 23–44.
  19. Hellenic Statistical Authority. (2009). 2001 Population and Housing Census of Greece.
  20. Hellenic Statistical Authority. (2014). 2011 Population and Housing Census of Greece (Issue April). https://www.statistics.gr/en/2011-census-pop-hous
  21. Holloway, S. R., Schumacher, J., & Redmond, R. L. (1997). People and place: dasymetric mapping using Arc/Info. Cartographic Design Using ArcView and Arc/Info, 1–11.
  22. Karunarathne, A., & Lee, G. (2019). Estimating hilly areas population using a dasymetric mapping approach: A case of Sri Lanka’s highest mountain range. ISPRS International Journal of Geo-Information, 8(4). https://doi.org/10.3390/ijgi8040166
  23. Kim, H., & Yao, X. (2010). Pycnophylactic interpolation revisited: Integration with the dasymetric-mapping method. International Journal of Remote Sensing, 31(21), 5657–5671. https://doi.org/10.1080/01431161.2010.496805
  24. Kubíček, P., Konečný, M., Stachoň, Z., Shen, J., Herman, L., Řezník, T., Staněk, K., Štampach, R., & Leitgeb, Š. (2019). Population distribution modelling at fine spatio-temporal scale based on mobile phone data. International Journal of Digital Earth, 12(11), 1319–1340. https://doi.org/10.1080/17538947.2018.1548654
  25. Lam, N. S. N. (1983). Spatial interpolation methods: A review. The American Cartographer, 10(2), 129–150. https://doi.org/10.1559/152304083783914958
  26. Langford, M. (2006). Obtaining population estimates in non-census reporting zones: An evaluation of the 3-class dasymetric method. Computers, Environment and Urban Systems, 30(2), 161–180. https://doi.org/10.1016/j.compenvurbsys.2004.07.001
  27. Langford, M. (2007). Rapid facilitation of dasymetric-based population interpolation by means of raster pixel maps. Computers, Environment and Urban Systems, 31(1), 19–32. https://doi.org/10.1016/j.compenvurbsys.2005.07.005
  28. Langford, M. (2013). An evaluation of small area population estimation techniques using open access ancillary data. Geographical Analysis, 45(3), 324–344. https://doi.org/10.1111/gean.12012
  29. Lin, J., & Cromley, R. G. (2015). Evaluating geo-located Twitter data as a control layer for areal interpolation of population. Applied Geography, 58, 41–47. https://doi.org/10.1016/j.apgeog.2015.01.006
  30. Liu, X., & Martinez, A. (2019). Areal Interpolation Using Parcel and Census Data in Highly Developed Urban Environments. ISPRS International Journal of Geo-Information, 8(7), 302. https://doi.org/10.3390/ijgi8070302
  31. Lwin, K. K., & Murayama, Y. (2009). A GIS approach to estimation of building population for micro-spatial analysis. Transactions in GIS, 13(4), 401–414. https://doi.org/10.1111/j.1467-9671.2009.01171.x
  32. Mennis, J. (2003). Generating Surface Models of Population Using Dasymetric Mapping*. The Professional Geographer, 55(1), 31–42. https://doi.org/10.1111/0033-0124.10042
  33. Mennis, J. (2009). Dasymetric mapping for estimating population in small areas. Geography Compass, 3(2), 727–745. https://doi.org/10.1111/j.1749-8198.2009.00220.x
  34. Mennis, J., & Hultgren, T. (2006). Intelligent dasymetric mapping and its application to areal interpolation. Cartography and Geographic Information Science, 33(3), 179–194. https://doi.org/10.1559/152304006779077309
  35. Openshaw, S. (1984). The modifiable areal unit problem. In Concepts and Techniques in Modern Geography (CATMOG 38). Geo Books.
  36. OSM Contributors. (2023). OSM Map Features. https://wiki.openstreetmap.org/wiki/Map_features
  37. Padgham, M., Lovelace, R., Salmon, M., & Rudis, B. (2017). Osmdata. The Journal of Open Source Software, 2(14), 305. https://doi.org/10.21105/joss.00305
  38. Pajares, E., Nieto, R. M., Meng, L., & Wulfhorst, G. (2021). Population disaggregation on the building level based on outdated census data. ISPRS International Journal of Geo-Information, 10(10). https://doi.org/10.3390/ijgi10100662
  39. Papanikolaou, P. V., & Mitsi, T. K. (2020). Analysis of population dynamics of the regional unit of Chania using remote sensing and census data. European Journal of Geography, 11(4), 110–125. https://doi.org/10.48088/EJG.P.PAP.11.4.110.125
  40. Paraskevopoulos, Y., Bardosa, A., & Photis, Y. N. (2019). Eploring the Impact of Network Configuration and Transport Accessibility on Population Dynamics. The Case of Naxos Island, Greece. European Journal of Geography, 10(4), 177–194.
  41. Park, J., Zhang, H., Han, S. Y., Nara, A., & Tsou, M. H. (2020). Estimating Hourly Population Distribution Patterns at High Spatiotemporal Resolution in Urban Areas Using Geo-Tagged Tweets and Dasymetric Mapping. 11th International Conference on Geographic Information Science (GIScience 2021), 177(10), 1–16. https://doi.org/10.4230/LIPIcs.GIScience.2021.I.10
  42. Pebesma, E. (2018). Simple features for R: Standardized support for spatial vector data. R Journal, 10(1), 439–446. https://doi.org/10.32614/rj-2018-009
  43. Peng, Z., Wang, R., Liu, L., & Wu, H. (2020). Fine-scale dasymetric population mapping with mobile phone and building use data based on grid voronoi method. ISPRS International Journal of Geo-Information, 9(6). https://doi.org/10.3390/ijgi9060344
  44. Photis, Y. N., & Sirigos, S. A. (2015). Scenario-Based Location of Ambulances for Spatiotemporal Clusters of Events and Stohastic Vehicle Availability. A Decision Support Systems Approach. European Journal of Geography, 6(4), 59-75. https://eurogeojournal.eu/index.php/egj/article/view/405/295
  45. Prener, C., & Revord, C. (2019). areal: An R package for areal weighted interpolation. Journal of Open Source Software, 4(37), 1221. https://doi.org/10.21105/joss.01221
  46. Qiu, F., Zhang, C., & Zhou, Y. (2012). The development of an areal interpolation ArcGIS extension and a comparative study. GIScience and Remote Sensing, 49(5), 644–663. https://doi.org/10.2747/1548-1603.49.5.644
  47. R Core Team. (2015). R: a Language and Environment for Statistical Computing. http://www.r-project.org/
  48. RStudio, I. (2013). Easy web applications in R. RStudio Inc. https://shiny.rstudio.com/
  49. Sleeter, R., & Gould, M. (2008). Geographic information system software to remodel population data using dasymetric mapping methods. US Geological Survey, Techniques and Methods, 11-C2, 1–15. http://pubs.usgs.gov/tm/tm11c2/
  50. Tenerelli, P., Gallego, J. F., & Ehrlich, D. (2015). Population density modelling in support of disaster risk assessment. International Journal of Disaster Risk Reduction, 13, 334–341. https://doi.org/10.1016/j.ijdrr.2015.07.015
  51. Wu, S., Qiu, X., & Wang, L. (2005). Population Estimation Methods in GIS and Remote Sensing: A Review. GIScience & Remote Sensing, 42(1), 80–96. https://doi.org/10.2747/1548-1603.42.1.80
  52. Younes, A., Ahmad, A., Hanjagi, A. D., & Nair, A. M. (2023). Understanding Dynamics of Land Use & Land Cover Change Using GIS & Change Detection Techniques in Tartous , Syria. European Journal of Geography, 14(3), 20–41. https://doi.org/10.48088/ejg.a.you.14.3.020.041
  53. Zandbergen, P. A., & Ignizio, D. A. (2010). Comparison of Dasymetric Mapping Techniques for Small-Area Population Estimates. Cartography and Geographic Information Science, 37(3), 199–214. https://doi.org/10.1559/152304010792194985