A three-weight surface modeling approach for optimizing small-scale population disaggregation

Abstract

In recent years, fine-scale gridded population data has been widely adopted for assessing and monitoring the Sustainable Development Goals (SDGs). However, the existing population disaggregation techniques struggle to generate precise population grids for small areas with scarce data. To address this, we have introduced a novel, lightweight population gridding technique that integrates dasymetric mapping and point-based surface modeling, titled three-weight surface modeling. This method comprises three weights, each offering a unique perspective on population spatial heterogeneity. The first weight, termed building-volume weight, is equivalent to the preliminary results of assigning population based on building volume data. The second weight, termed POI-center weight, comprises POI (Point of Interest) categories and aggregation patterns, aiming to articulate high-density population centers. It is computed using the neighborhood accumulation rule of Spearman’s correlation coefficients between POIs and population size. The third weight, termed POI-distance weight, represents varying decay rates of population with distance from high-density centers. This three-weight surface model facilitates dynamic adjustment of parameters to refine the building-volume weight according to the remaining POI-related weights, thereby generating a more precise population surface. Our analysis of the census population and the disaggregation outcomes from 544 villages in three counties of southern Guizhou Province, China (namely, Huishui, Luodian, and Pingtang) revealed that the three-weight surface model using local parameter groups outperformed individual dasymetric mapping or point-based surface modeling in terms of accuracy. Also, the 10 m population grid generated by this local parameter model (LPTW-POP) presented greater resolution and fewer errors (RMSE of 1109, MAE of 422, and MRE of 0.2630) compared to commonly use gridded population datasets like LandScan, WorldPop, and GHS-POP.

Graphical abstract

Keywords

population disaggregation / dasymetric mapping / surface modeling / Points-of-Interest (POIs)

References

[1] Alahmadi M, Atkinson P, Martin D (2013). Estimating the spatial distribution of the population of Riyadh, Saudi Arabia using remotely sensed built land cover and height data.Comput Environ Urban Syst, 41: 167–176

[2] Bakillah M, Liang S, Mobasheri A, Jokar Arsanjani J, Zipf A (2014). Fine-resolution population mapping using OpenStreetMap points-of-interest.Int J Geogr Inf Sci, 28(9): 1940–1963

[3] Baynes J, Neale A, Hultgren T (2022). Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas.Earth Syst Sci Data, 14(6): 2833–2849

[4] Bracken I, Martin D (1989). The generation of spatial population distributions from census centroid data.Environment and Planning A: Economy and Space, 21(4): 537–543

[5] Cartagena-Colón M, Mattei H, Wang C (2022). Dasymetric mapping of population using land cover data in JBNERR, Puerto Rico during 1990–2010.Land (Basel), 11(12): 2301

[6] Chen Y, Xu C, Ge Y, Zhang X, Zhou Y (2024). A 100 m gridded population dataset of China’s seventh census using ensemble learning and big geospatial data.Earth Syst Sci Data, 16(8): 3705–3718

[7] CIESIN (2018). Gridded Population of the World, Version 4 (GPWv4): Population Count, Revision 11. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC)

[8] Eicher C L, Brewer C A (2001). Dasymetric mapping and areal interpolation: implementation and evaluation.Cartogr Geogr Inf Sci, 28(2): 125–138

[9] European Commission (2023). GHSL data package 2023. Publications Office of the European Union, Luxembourg

[10] Fisher P F, Langford M (1995). Modelling the errors in areal interpolation between zonal systems by Monte Carlo simulation.Environment and Planning A: Economy and Space, 27(2): 211–224

[11] Gervasoni L, Fenet S, Perrier R, Sturm P (2018). Convolutional neural networks for disaggregated population mapping using open data. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)

[12] Goodchild M F, Anselin L, Deichmann U (1993). A framework for the areal interpolation of socioeconomic data.Environment and Planning A: Economy and Space, 25(3): 383–397

[13] Goodchild M F, Lam N (1980). Areal interpolation: a variant of the traditional spatial problem.Geo-Processing, 1(3): 297–312

[14] Harris R J, Longley P A (2000). New data and approaches for urban analysis: modelling residential densities.Trans GIS, 4(3): 217–234

[15] Karunarathne A, Lee G (2019). Estimating hilly areas population using a dasymetric mapping approach: a case of Sri Lanka’s highest mountain range.ISPRS Int J Geoinf, 8(4): 166

[16] Lam N S N (1983). Spatial interpolation methods: a review.Am Cartogr, 10(2): 129–150

[17] Langford M (2006). Obtaining population estimates in non-census reporting zones: an evaluation of the 3-class dasymetric method.Comput Environ Urban Syst, 30(2): 161–180

[18] Langford M (2013). An evaluation of small area population estimation techniques using open access ancillary data.Geogr Anal, 45(3): 324–344

[19] Langford M, Unwin D J (1994). Generating and mapping population density surfaces within a geographical information system.Cartogr J, 31(1): 21–26

[20] Li X, Zhou W (2018). Dasymetric mapping of urban population in China based on radiance corrected DMSP-OLS nighttime light and land cover data.Sci Total Environ, 643: 1248–1256

[21] Mei Y, Gui Z, Wu J, Peng D, Li R, Wu H, Wei Z (2022). Population spatialization with pixel-level attribute grading by considering scale mismatch issue in regression modeling.Geo Spat Inf Sci, 25(3): 365–382

[22] Mennis J (2003). Generating surface models of population using dasymetric mapping.Prof Geogr, 55(1): 31–42

[23] Mennis J (2009). Dasymetric mapping for estimating population in small areas.Geogr Compass, 3(2): 727–745

[24] Mennis J, Hultgren T (2006). Intelligent dasymetric mapping and its application to areal interpolation.Cartogr Geogr Inf Sci, 33(3): 179–194

[25] Monteiro M, Martins B, Murrieta-Flores P, Pires J M (2019). Spatial disaggregation of historical census data leveraging multiple sources of ancillary information.ISPRS Int J Geoinf, 8(8): 327

[26] Moxey A, Allanson P (1994). Areal interpolation of spatially extensive variables: a comparison of alternative techniques.Int J Geogr Inf Syst, 8(5): 479–487

[27] Palacios-Lopez D, Esch T, MacManus K, Marconcini M, Sorichetta A, Yetman G, Zeidler J, Dech S, Tatem A J, Reinartz P (2022). Towards an improved large-scale gridded population dataset: a pan-european study on the integration of 3D settlement data into population modelling.Remote Sens (Basel), 14(2): 325

[28] Pavía J M, Cantarino I (2017). Can dasymetric mapping significantly improve population data reallocation in a dense urban area.Geogr Anal, 49(2): 155–174

[29] Petrov A (2012). One hundred years of dasymetric mapping: back to the origin.Cartogr J, 49(3): 256–264

[30] Psyllidis A, Gao S, Hu Y, Kim E-K, McKenzie G, Purves R, Yuan M, Andris C (2022). Points of Interest (POI): a commentary on the state of the art, challenges, and prospects for the future.Comput Urban Sci, 2(1): 20

[31] Qiu Y, Zhao X, Fan D, Li S, Zhao Y (2022). Disaggregating population data for assessing progress of SDGs: methods and applications.Int J Digit Earth, 15(1): 2–29

[32] Rose A, McKee J, Sims K, Bright E, Reith A, Urban M (2021). LandScan Global 2020 [Global]. Oak Ridge National Laboratory

[33] Šimbera J (2020). Neighborhood features in geospatial machine learning: the case of population disaggregation.Cartogr Geogr Inf Sci, 47(1): 79–94

[34] Sinha P, Gaughan A E, Stevens F R, Nieves J J, Sorichetta A, Tatem A J (2019). Assessing the spatial sensitivity of a random forest model: application in gridded population modeling.Comput Environ Urban Syst, 75: 132–145

[35] Su M D, Lin M C, Hsieh H I, Tsai B W, Lin C H (2010). Multi-layer multi-class dasymetric mapping to estimate population distribution.Sci Total Environ, 408(20): 4807–4816

[36] Tatem A J (2017). WorldPop, open data for spatial demography.Sci Data, 4(1): 170004

[37] Thomson D R, Stevens F R, Chen R, Yetman G, Sorichetta A, Gaughan A E (2022). Improving the accuracy of gridded population estimates in cities and slums to monitor SDG 11: evidence from a simulation study in Namibia.Land Use Policy, 123: 106392

[38] Tobler W R (1979). Smooth pycnophylactic interpolation for geographical regions.J Am Stat Assoc, 74(367): 519–530

[39] United Nations (2021). Global Population Growth and Sustainable Development. United Nations Department of Economic and Social Affairs, Population Division

[40] Ural S, Hussain E, Shan J (2011). Building population mapping with aerial imagery and GIS data.Int J Appl Earth Obs Geoinf, 13(6): 841–852

[41] Weber E M, Seaman V Y, Stewart R N, Bird T J, Tatem A J, McKee J J, Bhaduri B L, Moehl J J, Reith A E (2018). Census-independent population mapping in northern Nigeria.Remote Sens Environ, 204: 786–798

[42] Wu W B, Ma J, Banzhaf E, Meadows M E, Yu Z W, Guo F X, Sengupta D, Cai X X, Zhao B (2023). A first Chinese building height estimate at 10 m resolution (CNBH-10 m) using multi-source earth observations and machine learning.Remote Sens Environ, 291: 113578

[43] Yuan Y, Smith R M, Limp W F (1997). Remodeling census population with spatial information from LandSat TM imagery.Comput Environ Urban Syst, 21(3−4): 245–258

[44] Zeifman L, Hertog S, Kantorova V, Wilmoth J (2022). A World of 8 Billion. United Nations Department of Economic and Social Affairs, Population Division

[45] Zhang C, Qiu F (2011). A point-based intelligent approach to areal interpolation.Prof Geogr, 63(2): 262–276

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