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 $\rho$ 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.

翻译：在疾病风险空间分析中，许多研究者（特别是印度尼西亚的研究者）仍将人口密度建模为总人口与行政区划面积的比值。这种模型过度简化了问题，因为它涵盖了大量无人居住区域，而模型应聚焦于有人居住的区域。本研究利用卫星影像的聚落制图来聚焦模型并计算聚落面积。据我们检索，尚未发现专门比较使用聚落制图与行政区划面积来建模人口密度以计算其与疾病发病率相关性的研究。本研究利用印度尼西亚中爪哇省和东爪哇省的结核病发病率数据，对两种模型进行了比较。研究表明，使用行政区划密度时，斯皮尔曼相关系数$\rho$被视为"一般"（0.566，p<0.01），而使用聚落密度时则为"中等偏强"（0.673，p<0.01）。根据霍特林t检验，该差异具有显著性。基于这一结果，我们鼓励研究者使用聚落制图来改进疾病风险空间分析中的人口密度建模。本研究使用和生成的资源已在https://github.com/mirzaalimm/PopulationDensityVsDisease上公开。