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. 2016 Jun 2;11(6):e0156808.
doi: 10.1371/journal.pone.0156808. eCollection 2016.

Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands

Filip Biljecki  1 , Ken Arroyo Ohori  1 , Hugo Ledoux  1 , Ravi Peters  1 , Jantien Stoter  1
Affiliations

Population Estimation Using a 3D City Model: A Multi-Scale Country-Wide Study in the Netherlands

Filip Biljecki et al. PLoS One. .

Abstract

The remote estimation of a region's population has for decades been a key application of geographic information science in demography. Most studies have used 2D data (maps, satellite imagery) to estimate population avoiding field surveys and questionnaires. As the availability of semantic 3D city models is constantly increasing, we investigate to what extent they can be used for the same purpose. Based on the assumption that housing space is a proxy for the number of its residents, we use two methods to estimate the population with 3D city models in two directions: (1) disaggregation (areal interpolation) to estimate the population of small administrative entities (e.g. neighbourhoods) from that of larger ones (e.g. municipalities); and (2) a statistical modelling approach to estimate the population of large entities from a sample composed of their smaller ones (e.g. one acquired by a government register). Starting from a complete Dutch census dataset at the neighbourhood level and a 3D model of all 9.9 million buildings in the Netherlands, we compare the population estimates obtained by both methods with the actual population as reported in the census, and use it to evaluate the quality that can be achieved by estimations at different administrative levels. We also analyse how the volume-based estimation enabled by 3D city models fares in comparison to 2D methods using building footprints and floor areas, as well as how it is affected by different levels of semantic detail in a 3D city model. We conclude that 3D city models are useful for estimations of large areas (e.g. for a country), and that the 3D approach has clear advantages over the 2D approach.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Datasets used in this research: census neighbourhoods with building footprints.
(Left side:) The Netherlands divided into more than 12 thousand neighbourhoods; and (right side:) two zoomed-in urban areas, where building footprints are visible along with the information on their use (residential share). Note that the maps on the right side show large variations in population density despite neighbourhoods being similarly urbanised. The less populated areas have many non-residential buildings, e.g. industrial and university buildings, showing that information on their use is crucial, and it significantly impacts the quality of the population estimation. The population density classes are divided into quantiles.
Fig 2
Fig 2. Census neighbourhoods statistics.
The plots expose substantial housing differences among the neighbourhoods across the country. Derived from data (c) Kadaster / Centraal Bureau voor de Statistiek, 2015.
Fig 3
Fig 3. Example of the 3D city model.
This example shows a part of the city of Delft, constructed from open data of the Government of the Netherlands ((c) Kadaster and (c) Actueel Hoogtebestand Nederland; see S3 Fig for the illustration of the elevation data).
Fig 4
Fig 4. Multi-LOD data used for the experiments.
Different granularities, which reflect the different grades of data available in practice. The blue space indicates residential space (proxy for population) as considered for each LOD, which differs depending on the geometry and semantics, and ultimately affects the performance of the methods. In our work we benchmark the performance of each grade of the data for the purpose of estimating the population.
Fig 5
Fig 5. The two population estimation methods used.
In this study we employ both methods, and for the residential capacity we use three different indicators in parallel: building footprint area, floorspace area, and building volume. Our work determines the usability of each of the type of geographic information for this purpose.
Fig 6
Fig 6. The Dutch statistical hierarchy, and our hybrid multi-scale approach.
The hybrid approach refers to both the disaggregation and statistical approach, while multiple scales refer to the level of the statistical units. Statistics of the units obtained from data (c) Kadaster / Centraal Bureau voor de Statistiek, 2015. The provinces are not shown because they have not been considered in our work, and the data refer to the situation in 2015.
Fig 7
Fig 7. Observed (actual data from the government census) vs predicted scatter plots of the 9 input datasets in the D1 method.
The performance of the models depends on the population density of the target area. The lower density refers to areas with the population density lower than the median of all neighbourhoods, and the higher those areas which are denser than the median, indicating urbanised areas. Notice the outliers in the estimations (a) that do not take advantage of the semantics—those represent highly industrialised areas without inhabitants or with sparse population. Furthermore, in the experiments carried out with fine-grade data most of the outliers are caused by input data (e.g. mislabelled residential use of a non-residential building) and by districts in which housing standards highly deviate from the average. Observed data (c) Centraal Bureau voor de Statistiek, Den Haag/Heerlen, 2015.
Fig 8
Fig 8. The relations between the errors, population density, and living space per statistical neighbourhood.
The errors in the model are from the experiment D1/LOD1c. Data (c) Kadaster / Centraal Bureau voor de Statistiek, 2015.
See this image and copyright information in PMC

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