PoD: A Web Tool for Population Downscaling Using Areal Interpolation and Volunteered Geographic Information
Published 2023-10-25
Keywords
- population downscaling,
- areal interpolation,
- web tool,
- GIS
How to Cite
Copyright (c) 2023 Marios Batsaris, Sofia Zafeirelli, Michail Vaitis, Dimitris Kavroudakis
This work is licensed under a Creative Commons Attribution 4.0 International License.
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
- 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
- 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
- Batsaris, M. (2021). populR: Population Down-Scaling in R. CRAN. https://cran.r-project.org/web/packages/populR/
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Goodchild, M. F., & Siu-Ngan Lam, N. (1980). Areal interpolation: a variant of the traditional spatial problem. Geo-Processing, 1(3), 297–312.
- 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
- 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.
- Hellenic Statistical Authority. (2009). 2001 Population and Housing Census of Greece.
- Hellenic Statistical Authority. (2014). 2011 Population and Housing Census of Greece (Issue April). https://www.statistics.gr/en/2011-census-pop-hous
- 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.
- 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
- 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
- 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
- Lam, N. S. N. (1983). Spatial interpolation methods: A review. The American Cartographer, 10(2), 129–150. https://doi.org/10.1559/152304083783914958
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Openshaw, S. (1984). The modifiable areal unit problem. In Concepts and Techniques in Modern Geography (CATMOG 38). Geo Books.
- OSM Contributors. (2023). OSM Map Features. https://wiki.openstreetmap.org/wiki/Map_features
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- R Core Team. (2015). R: a Language and Environment for Statistical Computing. http://www.r-project.org/
- RStudio, I. (2013). Easy web applications in R. RStudio Inc. https://shiny.rstudio.com/
- 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/
- 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
- 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
- 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
- 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