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Accurately measuring the characteristics of spatial clusters and changes in urban land surface temperature (LST) provides essential data that assist in urban heat island effect mitigation and sustainable urban development. Previous studies on the thermal environment often focused on the identification and spatial distribution of land surface temperature values and the lack of quantitative research on the LST spatial cluster characteristics, making it difficult to determine where mitigation strategies can be best applied to reduce high-temperature cluster (HH) areas and increase urban low-temperature cluster (LL) areas. Based on remote sensing (RS) images and geographic information system (GIS) technology, the cluster classification and spatial cluster characteristics analysis methods were used in this research to quantitatively assess the LST spatial cluster characteristics in Huaiyin District, Jinan City in 2000, 2005, 2010, 2015, 2020, and 2024. The results show the following: (1) The LST exhibited significant spatial cluster characteristics, with a strong correlation between the LST spatial cluster areas and their spatial locations. The spatial distributions of the HH and LL areas showed contrasts from north to south and west to east. (2) Decreasing temperature transformations were mainly located in new areas covered by water bodies and vegetation, while increasing temperature transformations were mainly located within re-developed built-up areas in the old urban area and in the newly built urban growth areas. The HH areas were larger, simpler in patch shape, and had more aggregated spatial distributions than the LL areas. Additionally, the barycentre distribution and migration trajectory of the HH areas were closely related to urban development planning. These quantitative results provide a scientific basis for understanding the urban LST spatial cluster characteristics, thus quantifying the core problem areas of urban planning and thermal environment regulation policies.

Spatial cluster detection is one of the focus areas of spatial analysis, whose objective is the identification of clusters from spatial distributions of point events aggregated in districts with small areas. Choi et al. (2018) formulated cluster detection as a parameter estimation problem to leverage the parameter selection capability of the sparse modeling method called the generalized fused lasso. Although this work is superior to conventional methods for detecting multiple clusters, its estimation results are limited to point estimates. This study therefore extended the above work as a Bayesian cluster detection method to describe the probabilistic variations of clustering results. The proposed method combines multiple sparsity-inducing priors and encourages sparse solutions induced by the generalized fused lasso. Evaluations were performed with simulated and real-world distributions of point events to demonstrate that the proposed method provides new information on the quantified reliabilities of clustering results at the district level while achieving comparable detection performances to that of the previous work.

Background: In 2013, an unusually high incidence of biliary tract cancer among current or former workers of the offset color proof printing department of a printing company in Osaka, Japan, was reported. The purpose of this study was to examine whether distance from the printing factory was associated with incidence of biliary tract cancer and whether incident biliary tract cancer cases clustered around the printing factory in Osaka using population-based cancer registry data. Methods: We estimated the age-standardized incidence ratio of biliary tract cancer according to distance from this printing factory. We also searched for clusters of biliary tract cancer incidence using spatial scan statistics. Results: We did not observe statistically significantly high or low standardized incidence ratios for residents in each area categorized by distance from the printing factory for the entire sample or for either sex. The scan statistics did not show any statistically significant clustering of biliary tract cancer incidence anywhere in Osaka prefecture in 2004-2007. Conclusions: There was no statistically significant clustering of biliary tract cancer incidence around the printing factory or in any other areas in Osaka, Japan, between 2004 and 2007. To date, even if some substances have diffused outside this source factory, they do not appear to have influenced the incidence of biliary tract cancer in neighboring residents.

This paper operationalizes the idea of a local indicator of spatial association for the situation where the variables of interest are binary. This yields a conditional version of a local join count statistic. The statistic is extended to a bivariate and multivariate context, with an explicit treatment of co-location. The approach provides an alternative to point pattern-based statistics for situations where all potential locations of an event are available (e.g., all parcels in a city). The statistics are implemented in the open-source GeoDa software and yield maps of local clusters of binary variables, as well as co-location clusters of two (or more) binary variables. Empirical illustrations investigate local clusters of house sales in Detroit in 2013 and 2014, and urban design characteristics of Chicago census blocks in 2017.

We compared a local clustering and a cluster morphology statistic using anthrax outbreaks in large (cattle) and small (sheep and goats) domestic ruminants across Kazakhstan. The Getis-Ord (Gi*) statistic and a multidirectional optimal ecotope algorithm (AMOEBA) were compared using 1st, 2nd and 3rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and Gi* tests. A logistic regression was used to define a risk surface for anthrax outbreaks and to compare agreement between clustering methodologies. Tests revealed differences in the spatial distribution of clusters as well as the total number of clusters in large ruminants for AMOEBA (n = 149) and for small ruminants (n = 9). In contrast, Gi* revealed fewer large ruminant clusters (n = 122) and more small ruminant clusters (n = 61). Significant environmental differences were found between groups using the Kruskall-Wallis and Mann-Whitney U tests. Logistic regression was used to model the presence/absence of anthrax outbreaks and define a risk surface for large ruminants to compare with cluster analyses. The model predicted 32.2% of the landscape as high risk. Approximately 75% of AMOEBA clusters corresponded to predicted high risk, compared with ~64% of Gi* clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while Gi* tended to predict larger, circular clusters. Here we provide an evaluation of both tests and a discussion of the use of each to detect environmental conditions associated with anthrax outbreak clusters in domestic livestock. These findings illustrate important differences in spatial statistical methods for defining local clusters and highlight the importance of selecting appropriate levels of data aggregation.

Background Women's cancers, represented by breast and gynecologic cancers, are emerging as a significant threat to women's health, while previous studies paid little attention to the spatial distribution of women's cancers. This study aims to conduct a spatio-temporal epidemiology analysis on breast, cervical and ovarian cancers in China, thus visualizing and comparing their epidemiologic trends and spatio-temporal changing patterns. Methods Data on the incidence and mortality of women’s cancers between January 2010 and December 2015 were obtained from the National Cancer Registry Annual Report. Linear tests and bar charts were used to visualize and compare the epidemiologic trends. Two complementary spatial statistics (Moran’s I statistics and Kulldorff’s space–time scan statistics) were adopted to identify the spatial–temporal clusters. Results The results showed that the incidence and mortality of breast cancer displayed slow upward trends, while that of cervical cancer increase dramatically, and the mortality of ovarian cancer also showed a fast increasing trend. Significant differences were detected in incidence and mortality of breast, cervical and ovarian cancer across east, central and west China. The average incidence of breast cancer displayed a high-high cluster feature in part of north and east China, and the opposite traits occurred in southwest China. In the meantime, the average incidence and mortality of cervical cancer in central China revealed a high-high cluster feature, and that of ovarian cancer in northern China displayed a high-high cluster feature. Besides, the anomalous clusters were also detected based on the space–time scan statistics. Conclusion Regional differences were detected in the distribution of women’s cancers in China. An effective response requires a package of coordinated actions that vary across localities regarding the spatio-temporal epidemics and local conditions.

Research to date has provided limited insight into the complexity of water-borne pathogen transmission. Private well water supplies have been identified as a significant pathway in infectious disease transmission in both the industrialised and the developing world. Using over 90,000 private well water submission records representing approximately 30,000 unique well locations in south-eastern Ontario, Canada, a spatial analysis was performed in order to delineate clusters with elevated risk of E. coli contamination using 5 years of data (2008-2012). Analyses were performed for all years independently and subsequently compared to each other. Numerous statistically significant clusters were identified and both geographic stability and variation over time were examined. Through the identification of spatial and temporal patterns, this study provides the basis for future investigations into the underlying causes of bacterial groundwater contamination, while identifying geographic regions that merit particular attention to public health interventions and improvement of water quality.

Objectives Examine the degree of crime concentration at micro-places across six large cities, the spatial clustering of high and low crime micro-places within cities, the presence of outliers within those clusters, and extent to which there is stability and change in micro-place classification over time. Methods Using crime incident data gathered from six U.S. municipal police departments (Chicago, Los Angeles, New York City, Philadelphia, San Antonio, and Seattle) and aggregated to the street segment, Local Moran’s I is calculated to identify statistically significant high and low crime clusters across each city and outliers within those clusters that differ significantly from their local spatial neighbors. Results Within cities, the proportion of segments that are like their neighbors and fall within a statistically significant high or low crime cluster are relatively stable over time. For all cities, the largest proportion of street segments fell into the same classification over time (47.5% to 69.3%); changing segments were less common (4.7% to 20.5%). Changing clusters (i.e., segments that fell into both low and high clusters during the study) were rare. Outliers in each city reveal statistically significant street-to-street variability. Conclusions The findings revealed similarities across cities, including considerable stability over time in segment classification. There were also cross-city differences that warrant further investigation, such as varying levels of spatial clustering. Understanding stable and changing clusters and outliers offers an opportunity for future research to explore the mechanisms that shape a city’s spatiotemporal crime patterns to inform strategic resource allocation at smaller spatial scales.

Fasciolosis caused by Fasciola hepatica is a widespread parasitic disease in cattle farms. The aim of this study was to detect clusters of fasciolosis in dairy cow herds in Munster Province, Ireland and to identify significant climatic and environmental predictors of the exposure risk. In total, 1,292 dairy herds across Munster was sampled in September 2012 providing a single bulk tank milk (BTM) sample. The analysis of samples by an in-house antibody-detection enzyme-linked immunosorbent assay (ELISA), showed that 65% of the dairy herds (n = 842) had been exposed to F. hepatica. Using the Getis-Ord Gi* statistic, 16 high-risk and 24 low-risk (P <0.01) clusters of fasciolosis were identified. The spatial distribution of high-risk clusters was more dispersed and mainly located in the northern and western regions of Munster compared to the low-risk clusters that were mostly concentrated in the southern and eastern regions. The most significant classes of variables that could reflect the difference between high-risk and low-risk clusters were the total number of wet-days and rain-days, rainfall, the normalized difference vegetation index (NDVI), temperature and soil type. There was a bigger proportion of well-drained soils among the low-risk clusters, whereas poorly drained soils were more common among the high-risk clusters. These results stress the role of precipitation, grazing, temperature and drainage on the life cycle of F. hepatica in the temperate Irish climate. The findings of this study highlight the importance of cluster analysis for identifying significant differences in climatic and environmental variables between high-risk and low-risk clusters of fasciolosis in Irish dairy herds.

Background Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during the early stages of the pandemic can guide policies, inform allocation of control and prevention resources, and provide valuable baseline data to evaluate the effectiveness of interventions for mitigating health, economic and social impacts. Therefore, the objective of this study was to identify geographic disparities in COVID-19 testing, incidence, hospitalizations, and deaths during the first five months of the pandemic in Florida.  Methods Florida county-level COVID-19 data for the time period March-July 2020 were used to compute various COVID-19 metrics including testing rates, positivity rates, incidence risks, percent of hospitalized cases, hospitalization risks, case-fatality rates, and mortality risks. High or low risk clusters were identified using either Kulldorff’s circular spatial scan statistics or Tango’s flexible spatial scan statistics and their locations were visually displayed using QGIS. Results Visual examination of spatial patterns showed high estimates of all COVID-19 metrics for Southern Florida. Similar to the spatial patterns, high-risk clusters for testing and positivity rates and all COVID-19 outcomes (i.e. hospitalizations and deaths) were concentrated in Southern Florida. The distributions of these metrics in the other parts of Florida were more heterogeneous. For instance, testing rates for parts of Northwest Florida were well below the state median (11,697 tests/100,000 persons) but they were above the state median for North Central Florida. The incidence risks for Northwest Florida were equal to or above the state median incidence risk (878 cases/100,000 persons), but the converse was true for parts of North Central Florida. Consequently, a cluster of high testing rates was identified in North Central Florida, while a cluster of low testing rate and 1–3 clusters of high incidence risks, percent of hospitalized cases, hospitalization risks, and case fatality rates were identified in Northwest Florida. Central Florida had low-rate clusters of testing and positivity rates but it had a high-risk cluster of percent of hospitalized cases. Conclusions Substantial disparities in the spatial distribution of COVID-19 outcomes and testing and positivity rates exist in Florida, with Southern Florida counties generally having higher testing and positivity rates and more severe outcomes (i.e. hospitalizations and deaths) compared to Northern Florida. These findings provide valuable baseline data that is useful for assessing the effectiveness of preventive interventions, such as vaccinations, in various geographic locations in the state. Future studies will need to assess changes in spatial patterns over time at lower geographical scales and determinants of any identified patterns.