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.
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.
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.
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.
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.
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.
Table 1.
Median absolute percentage errors in the population estimates resulting from our experiments.
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.
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.