Detecting nominal variables’ spatial associations using conditional probabilities of neighboring surface objects’ categories

Abstract

a b s t r a c tHow to automatically mining the spatial association patterns in spatial data is a challenging task in spatial data mining. In this paper, we propose three indices that represent the per-class, inter-class, and overall spatial associations of a nominal variable, which are based on the conditional probabilities of surface object categories. These indices represent relative quantities and are normalized to the region [−1, 1], which more accord with the intuitive cognition of people. We present some algorithms for detecting spatial associations that are based on these indices. The proposed method can be regarded as an extension of join count statistics and Transiogram. Several constructive examples were used to illustrate the advantages of the new method. Using two real data sets, vegetation types in Qingxian, Shanxi, China and neural tube birth defects in Heshun, Shanxi, China, we ran comparative experiments with other commonly used methods, including join count statistics, co-location quotient, and Q(m) statistics. The experimental results show that the proposed method can detect more subtle spatial associations, and is not sensitive to the sequence of neighbors.

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

“…Fourth, future research should focus on integrating longer time series of remote sensing images to more accurately delineate the interactions between multiple ecological factors and their effects on urban forest PM 2.5 concentrations, while also using remote sensing data fusion methods to achieve high temporal and spatial resolution simultaneously at the city scale. Finally, the process of comparing quantitative impact factors with qualitative impact factors is subjective, because arbitrary methods of discretization (e.g., standard deviation, equal interval, Jenks, and quantile) may not characterize the actual associations between impact factors and PM 2.5 concentrations [55][56][57].…”

Utilizing spatial association analysis to determine the number of multiple grids for multiple-point statistics

“…It represents the relations between cells that are not 1-adjacent. A k can be recursively derived using the concept of relation composition or just using the distance between cells (Bai et al, 2016) in terms of the requirements of applications.…”

“…If JCS < 0, then cells from the same category tend to agglomerate in space (positive spatial association). If JCS > 0, then cells from different categories tend to agglomerate (negative spatial association) (Bai et al, 2016).…”

“…As well as significance testing, it is necessary to measure the degree of spatial association when estimating the target scale (Bai et al, 2016;Leslie and Kronenfeld, 2011). If the degree of spatial association is too small, the structural information at this scale can be ignored.…”

“…In this work, the JCS was modified to better reflect two extreme situations: completely positive and completely negative spatial associations (Bai et al, 2016). P(diff ) = N diff /N all is the probability of observing diff -joins, and P E (diff ) = E(N diff )/N all is the expected probability of observing diff -joins under the assumption of a random distribution.…”