This study highlights spatial associations of the hometown tax payment, which is a tax donation program to municipalities of taxpayers' favorite choices in Japan and is called as Furusato Nozei. The program has become an important source of revenue for low-funded municipalities. With increasing donations, the disparity between municipalities widens. To the best of our knowledge, thus far, no study has discussed the spatial characteristics from a quantitative perspective. In this study, a local indicator of spatial associations, the local Moran statistic, is used for hotspot analysis of donations made to each municipality. The results indicate the presence of spatial agglomerations, where taxpayers are aware of high return rates of their donations or reconstruction assistance for the Great East Japan Earthquake. The results also suggest that when adjacent to a municipality receiving the donation higher than the average, municipalities can be 7.8% more likely to become the same class.

Geographically weighted (GW) method is a type of spatial statistical framework. GW methods have been developed to tackle spatial heterogeneity in data, with a kernel that moves across geographical space. The GW method applies to a wide range of statistical analysis methods to explore the local geographical characteristics of data and its relationships in bivariate and multivariate data analysis. GW methods currently include (generalized) linear regression, summary statistics, and principal components analysis. They have further potentials to be extended to any statistical methods. To discuss future directions of GW method developments, we reviewed previous works regarding the state-of-art GW methods and available software and tools. As its customization is flexible, the GW method is feasible for any spatial phenomenon in cases where spatial heterogeneity is to be considered.

In recent years, the application of satellites and big data to the economic field has been expanding rapidly. In particular, it has become clear that the intensity of night light acquired by satellites is correlated with social and economic indicators such as gross domestic product, employment, population, and education in each country. In this paper, I first describe the method of calculating the night light intensity in Japan by prefecture and city. Next, in order to understand the versatility of the night light data, I examine the relationship between the intensity of night light at the city level and various socio-economic indicators and data published by public sectors, using Japan as a case study. Based on the results of the various analyses, it is assumed that night light can be used as a proxy variable for these various indicators. Finally, I describe the possibility of using night light to perform a rapid analysis on the stagnation of economic activity under sudden social events, such as the COVID-19 pandemic.