Neighborhood features in geospatial machine learning: the case of population disaggregation

Šimbera, Jan

doi:10.1080/15230406.2019.1618201

Cited by 9 publications

(4 citation statements)

References 51 publications

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“…Finally, hybrid methods combine the dasymetric method with weights from the statistical method to refine the population disaggregation, assuming that the weights represent the distribution of the population density within built-up areas. When compared to other methods, the hybrid method seems to produce better results at the expense of greater computational efforts [16,44,45].…”

Section: Introductionmentioning

confidence: 98%

Empiric recommendations for population disaggregation under different data scenarios

et al. 2022

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High-resolution population mapping is of high relevance for developing and implementing tailored actions in several fields: From decision making in crisis management to urban planning. Earth Observation has considerably contributed to the development of methods for disaggregating population figures with higher resolution data into fine-grained population maps. However, which method is most suitable on the basis of the available data, and how the spatial units and accuracy metrics affect the validation process is not fully known. We aim to provide recommendations to researches that attempt to produce high-resolution population maps using remote sensing and geospatial information in heterogeneous urban landscapes. For this purpose, we performed a comprehensive experimental research on population disaggregation methods with thirty-six different scenarios. We combined five different top-down methods (from basic to complex, i.e., binary and categorical dasymetric, statistical, and binary and categorical hybrid approaches) on different subsets of data with diverse resolutions and degrees of availability (poor, average and rich). Then, the resulting population maps were systematically validated with a two-fold approach using six accuracy metrics. We found that when only using remotely sensed data the combination of statistical and dasymetric methods provide better results, while highly-resolved data require simpler methods. Besides, the use of at least three relative accuracy metrics is highly encouraged since the validation depends on level and method. We also analysed the behaviour of relative errors and how they are affected by the heterogeneity of the urban landscape. We hope that our recommendations save additional efforts and time in future population mapping.

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Section: Introductionmentioning

confidence: 98%

Empiric recommendations for population disaggregation under different data scenarios

et al. 2022

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“…Both approaches have been applied in studies for the estimation of population in small areas, being driven by one or more multiple predictors that hint at the size of the population [ 5 , 21 ]. These predictors come in different forms and shapes and from different sources [ 22 ]. For example, land use classes and night time lights, derived from remote sensing techniques, are a common set of information that are used in population estimations [ 1 , 4 , 23 – 25 ].…”

Section: Introductionmentioning

confidence: 99%

Population estimation beyond counts—Inferring demographic characteristics

Szarka

Biljecki

2022

PLoS ONE

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Mapping population distribution at a fine spatial scale is essential for urban studies and planning. Numerous studies, mainly supported by geospatial and statistical methods, have focused primarily on predicting population counts. However, estimating their socio-economic characteristics beyond population counts, such as average age, income, and gender ratio, remains unattended. We enhance traditional population estimation by predicting not only the number of residents in an area, but also their demographic characteristics: average age and the proportion of seniors. By implementing and comparing different machine learning techniques (Random Forest, Support Vector Machines, and Linear Regression) in administrative areas in Singapore, we investigate the use of point of interest (POI) and real estate data for this purpose. The developed regression model predicts the average age of residents in a neighbourhood with a mean error of about 1.5 years (the range of average resident age across Singaporean districts spans approx. 14 years). The results reveal that age patterns of residents can be predicted using real estate information rather than with amenities, which is in contrast to estimating population counts. Another contribution of our work in population estimation is the use of previously unexploited POI and real estate datasets for it, such as property transactions, year of construction, and flat types (number of rooms). Advancing the domain of population estimation, this study reveals the prospects of a small set of detailed and strong predictors that might have the potential of estimating other demographic characteristics such as income.

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“…Even though the demand for high-resolution population data is increasing, detailed databases often remain scarce or unavailable, due to limitations on the collection of individual micro-data [10,11]. For confidentiality reasons, public data are aggregated [6,12], or could be available but not digitized [9,13].…”

Section: Introductionmentioning

confidence: 99%

“…Most recent studies propose to combine different methods to increase the accuracy, often starting with a dasymetric process [6]. The hybrid process combines a dasymetric analysis with a fit regression model, dasymetric techniques with machine-learning algorithms [11,30]. A two-step disaggregation is proposed based on a dasymetric processing and a weighted distribution method using 3D building information [2].…”

Section: Introductionmentioning

confidence: 99%

Population Disaggregation on the Building Level Based on Outdated Census Data

Pajares

Nieto

Meng

et al. 2021

IJGI

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A wide range of disciplines require population data with high spatial resolution. In particular, accessibility instruments for active mobility need data on the building access level. Data availability varies by context. Spatially detailed national census counts often present the challenge that they are outdated. Therefore, this study proposes a novel approach to hybrid population disaggregation. It updates outdated census tracts and disaggregates population on the building access level. Open and widely available data sets are used. A bottom-up population estimation for new development areas is combined with a top-down dasymetric mapping process to update outdated census tracts. A particular focus lies on the high flexibility of the developed procedure. Accordingly, users can utilize diverse data and adapt settings to a specific study context. Instead of requiring ubiquitous 3D building data, often unavailable free of charge, the approach suggests collecting building levels only in new development areas. The open-source software development was done using PostgreSQL/PostGIS as part of the co-creative development of the accessibility instrument GOAT in three German municipalities. A comparison with reference data from the population registry of one district was realized. On the building level, an R2 of 0.82, and on the grid level (100 m × 100 m), an R2 of 0.89 is reached. The approach stands out when land-use information is outdated; however, a spatially detailed census grid exists, but no ubiquitous 3D building information is available. Enhancements are proposed, such as improving the dasymetric mapping with machine learning and remote sensing techniques. Moreover, more reliable detection of new building development in already built-up areas is suggested to account better for urban densification.

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Neighborhood features in geospatial machine learning: the case of population disaggregation

Cited by 9 publications

References 51 publications

Empiric recommendations for population disaggregation under different data scenarios

Empiric recommendations for population disaggregation under different data scenarios

Population estimation beyond counts—Inferring demographic characteristics

Population Disaggregation on the Building Level Based on Outdated Census Data

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