Dataset Information

Projecting population distribution under depopulation conditions in Japan: scenario analysis for future socio-ecological systems.

ABSTRACT: This study develops a projection model of future population distribution on the basis of Japan's current depopulation trend and applies this model to scenario analyses that assume population compactification and dispersion. The model enables a description of population migration at two levels. First, municipal populations are projected using the cohort-component method, and second, the spatial distribution of populations within municipalities is projected at a 500 m grid resolution with the use of the gravity model. Based on the Japanese depopulation context and the country's National Spatial Strategy, the compact scenario predicts the formation of medium-scale regional urban areas (population centers located across Japan) and the concentration of people on high-density population areas within municipalities. Meanwhile, the dispersed scenario predicts the formation of more but smaller regional urban areas and the dispersion of the population to low-density areas. The simulated population distribution for 2050 reveals spatial change in population density and age structure, as well as an abundance of areas that were inhabited in 2015 but will be zero population areas by 2050. Overlay analysis of future land use maps and the simulated population distribution maps can contribute toward identifying areas where natural capital such as farmland and forest plantation should be managed but where there will be significant population loss by 2050.

SUBMITTER: Hori K

PROVIDER: S-EPMC7406701 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Projecting population distribution under depopulation conditions in Japan: scenario analysis for future socio-ecological systems.

Hori Keiko K Saito Osamu O Hashimoto Shizuka S Matsui Takanori T Akter Rumana R Takeuchi Kazuhiko K

Sustainability science 20200806 1

This study develops a projection model of future population distribution on the basis of Japan's current depopulation trend and applies this model to scenario analyses that assume population compactification and dispersion. The model enables a description of population migration at two levels. First, municipal populations are projected using the cohort-component method, and second, the spatial distribution of populations within municipalities is projected at a 500 m grid resolution with the use ...[more]

PMID: 32837576

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Project description:Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty.

Dataset Information

Projecting population distribution under depopulation conditions in Japan: scenario analysis for future socio-ecological systems.

Publications

Projecting population distribution under depopulation conditions in Japan: scenario analysis for future socio-ecological systems.

Similar Datasets

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