For the purely spatial analyses, the most likely cluster was detected for each year.
In the purely spatial analyses, the most likely clusters were in the Chikuho coal mining area (in 1999, 2002, 2003, 2004), the Kita-Kyushu industrial area (in 2000), and the Fukuoka urban area (in 2001).
The results of forest spatial analyses show that most tree species exhibit a high degree of spatial mingling; that the spatial distribution of small and large trees is clustered; and that horizontal and vertical forest structures are very irregular.
Based on our findings, the most appropriate geographical level to conduct spatial analyses and report incidence risk is the census tract level.
These two spatial analyses are among the most applicable ones in GIS because they are used in a wide range of use cases [28, 29], although they are also often used in conjunction with other analyses such as sky view factor [13, 30].
Thus, spatial analyses were performed in four stages (step by step): Unadjusted analysis, to identify and localize the most likely cluster of high incidence of ESRD.
Most spatial methods require an a priori definition of a spatial neighbourhood, also referred to as a spatial weight matrix or kernel (Bavaud 1998; Getis and Aldstadt 2002), and results of analyses are dependent on the spatial neighbourhood definition (e.g. Moilanen and Nieminen 2002).
Spatial analyses were conducted using GIS (ArcView).
Neighbourhood definitions will impact results of spatial analyses.
Spatial analyses were carried out using GIS software.
