Settlement Mapping for Population Density Modelling in Disease Risk Spatial Analysis
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| Format: | Preprint |
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2024
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| _version_ | 1866916166598918144 |
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| author | Mutasodirin, Mirza Alim Rizqullah, Rafi Dwi Setiyoko, Andie Arymurthy, Aniati Murni |
| author_facet | Mutasodirin, Mirza Alim Rizqullah, Rafi Dwi Setiyoko, Andie Arymurthy, Aniati Murni |
| contents | In disease risk spatial analysis, many researchers especially in Indonesia are still modelling population density as the ratio of total population to administrative area extent. This model oversimplifies the problem, because it covers large uninhabited areas, while the model should focus on inhabited areas. This study uses settlement mapping against satellite imagery to focus the model and calculate settlement area extent. As far as our search goes, we did not find any specific studies comparing the use of settlement mapping with administrative area to model population density in computing its correlation to a disease case rate. This study investigates the comparison of both models using data on Tuberculosis (TB) case rate in Central and East Java Indonesia. Our study shows that using administrative area density the Spearman's $ρ$ was considered as "Fair" (0.566, p<0.01) and using settlement density was "Moderately Strong" (0.673, p<0.01). The difference is significant according to Hotelling's t test. By this result we are encouraging researchers to use settlement mapping to improve population density modelling in disease risk spatial analysis. Resources used by and resulting from this work are publicly available at https://github.com/mirzaalimm/PopulationDensityVsDisease. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_12858 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Settlement Mapping for Population Density Modelling in Disease Risk Spatial Analysis Mutasodirin, Mirza Alim Rizqullah, Rafi Dwi Setiyoko, Andie Arymurthy, Aniati Murni Applications Populations and Evolution Quantitative Methods Methodology In disease risk spatial analysis, many researchers especially in Indonesia are still modelling population density as the ratio of total population to administrative area extent. This model oversimplifies the problem, because it covers large uninhabited areas, while the model should focus on inhabited areas. This study uses settlement mapping against satellite imagery to focus the model and calculate settlement area extent. As far as our search goes, we did not find any specific studies comparing the use of settlement mapping with administrative area to model population density in computing its correlation to a disease case rate. This study investigates the comparison of both models using data on Tuberculosis (TB) case rate in Central and East Java Indonesia. Our study shows that using administrative area density the Spearman's $ρ$ was considered as "Fair" (0.566, p<0.01) and using settlement density was "Moderately Strong" (0.673, p<0.01). The difference is significant according to Hotelling's t test. By this result we are encouraging researchers to use settlement mapping to improve population density modelling in disease risk spatial analysis. Resources used by and resulting from this work are publicly available at https://github.com/mirzaalimm/PopulationDensityVsDisease. |
| title | Settlement Mapping for Population Density Modelling in Disease Risk Spatial Analysis |
| topic | Applications Populations and Evolution Quantitative Methods Methodology |
| url | https://arxiv.org/abs/2403.12858 |