Spatial autocorrelation analysis of Covid-19 cases in South Kalimantan, Indonesia

Coronavirus Disease 2019 (COVID-19) is a new coronavirus that was discovered in Wuhan, China, at the end of 2019. In March 2020, the outbreak extended throughout the world, including Indonesia and one of its provinces, South Kalimantan. This rapid expansion should belinked to people’s mobility between regions, hence the linkage across regions must be examined. In South Kalimantan Province, the purpose of this research is to evaluate the distribution and relationship across regions in terms of the number of positive COVID-19 cases, the number of additional positive COVID-19 cases, and the number of COVID-19 patients under treatment. The spatial autocorrelation analysis with the Moran Index and Local Indicator of Spatial Autocorrelation (LISA) tests were used to determine the spatial autocorrelation between what and what using what data/where the data obtained? from March 22 to September 30, 2020. Based on the results of the Moran Index test, it is known that there is a spatial autocorrelation in the number of cases, the number of additional cases and the number of positive confirmed COVID-19 patients in treatment between one region and another neighboring area. While the results of the LISA Index test show that Balangan Regency, Hulu Sungai Tengah Regency, Hulu Sungai Utara Regency, Banjarmasin City, Tabalong Regency and Banjar Regency affect the level of COVID-19 cases in their respective neighboring areas. Therefore, there is a need for policies to control community mobility in those spatially correlated areas and increase testing and tracing to control the spread of COVID-19 cases in South Kalimantan Province.

SPATIAL AUTOCORRELATION ANALYSIS OF HOUSING DISTRIBUTION IN JOHOR BAHRU

Geographic location naturally generates spatial patterns that are either clustered, dispersed, or random. Moreover, Tobler’s First Law of Geography is essentially a testable assumption in the concept where geographic location matters and one method for quantifying Tobler’s law of geography is through measures of spatial autocorrelation. Therefore, the purpose of this study is to identify the spatial patterns of housing distribution in Johor Bahru through the spatial autocorrelation method. The result of the global spatial autocorrelation analysis demonstrates a high degree of clustering within the housing distribution, as well as the identification of a clustered pattern with a highly positive Moran’s I value of 0.995207. Following that, the LISA cluster map successfully identified individual clusters of each housing unit with their neighbours through the red and blue colours displayed on the map, as well as revealing home buyers’ preferences for a property in each location.

Identification of Shrinking Cities on the Main Island of Taiwan Based on Census Data and Population Registers: A Spatial Analysis

At the end of the 20th century, the phenomenon of urban shrinkage received widespread attention, with population decline as its core characteristic. In 2020, the Taiwanese population had negative growth and faced a low fertility rate and an aging population. This study used exploratory spatial data analysis to identify shrinking cities in Taiwan based on census data and population registers. The results indicated that Taiwan has 11 shrinking counties and 202 shrinking towns. Urban shrinkage occurred in the 1980s and continued from the suburbanization stage to the re-urbanization stage. Five types of spatial patterns in the 11 shrinking counties were observed. In the majority of the shrinking counties, towns with high population densities were unable to avoid shrinkage. A global spatial autocorrelation analysis indicated that shrinkage and non-shrinkage have become increasingly apparent at the town level since 2005. A local spatial autocorrelation analysis indicates that the spatial clustering of towns with population growth or decline from 2000 to 2020 has changed. Based on each town's development, a two-step cluster analysis was conducted in which all towns were divided into four categories. Shrinking towns exist in each category, but with a different proportion. Based on the results of two-step cluster analysis combined with spatial analysis, this study discovered that both urbanization and suburbanization cause shrinkage in Taiwan, but the affected localities are distinct. For most shrinking counties, their spatial model indicates a relationship between shrinking and the urbanization of their towns. Keelung City and Chiayi City have the most potential to reverse the shrinkage. This study helps authorities better manage growth and implement regional revitalization.

Risk Assessment and Factors Associated with Lung Cancer using GIS in Mae Ka Subdistrict, Muang District, Phayao Province, Thailand

Cancer is a major health problem in the developing countries. Variations of its incidence rate among geographical areas are due to various contributing factors. This study was performed to assess the spatial patterns of lung cancer incidence in the Mae Ka subdistrict, Muang district, Phayao province, based on lung cancer registry data and to determine geographical clusters. In this cross-sectional study, the cases of lung cancer were recorded from 2015 to 2020. Crude incidence rate was estimated based on age groups and sex in the province of the Mae Ka subdistrict. It uses spatial autocorrelation analysis (SAA) techniques to provide insight into the patterns, in terms of their geographical distributions and hotspot identification. Spatial autocorrelation analysis was performed in measuring the geographic patterns and clusters using GIS. In addition, local indicators of spatial association (LISA) and kernel density (KD) estimation were used to detect lung cancer hotspots using data at village level. Factors associated with the incidence of lung cancer was analyzed for behavior risk factors. Analysis of the spatial distribution of lung cancer shows significant differences from year to year and between different areas. The hotspot maps showed spatial trend patterns of lung cancer diffusion. Villages in the northern part revealed higher incidence. Furthermore, the spatial patterns during the years 2015, 2017 and 2019 were found to represent spatially clustered patterns, both at global and local scales. However, a clear spatial autocorrelation is observed, which can be of grate interest and importance to researchers for future epidemiological studies, and to policymakers for applying preventive measures.

Epidemiological characteristics and spatiotemporal analysis of hand-foot-mouth diseases from 2010 to 2019 in Zibo city, Shandong, China

Background Hand-foot-mouth disease (HFMD) is a global public health issues, especially in China. It has threat the health of children under 5 years old. The early recognition of high-risk districts and understanding of epidemic characteristics can facilitate health sectors to prevent the occurrence of HFMD effectively. Methods Descriptive analysis was used to summarize epidemic characteristics, and the spatial autocorrelation analysis and space-time scan analysis were utilized to explore distribution pattern of HFMD and identify hot spots with statistical significance. The result was presented in ArcMap. Results A total of 52,095 HFMD cases were collected in Zibo city from 1 Jan 2010 to 31 Dec 2019. The annual average incidence was 129.72/100,000. The distribution of HFMD was a unimodal trend, with peak from April to September. The most susceptible age group was children under 5 years old (92.46%), and the male-to-female ratio is 1.60: 1. The main clusters were identified in Zhangdian District from 12 April 2010 to 18 September 2012. Spatial autocorrelation analysis showed that the global spatial correlation in Zibo were no statistical significance, except in 2012, 2014, 2015, 2016 and 2018. Cold spots were gathered in Boshan county and Linzi district, while hot spots only in Zhangdian District in 2018, but other years were no significance. Conclusion Hot spots mainly concentrated in the central and surrounding city of Zibo city. We suggest that imminent public health planning and resource allocation should be focused within those areas.

Spatial-temporal heterogeneity and meteorological factors of hand-foot-and-mouth disease in Xinjiang, China from 2008 to 2016

The study aims to depict the temporal and spatial distributions of hand-foot-and-mouth disease (HFMD) in Xinjiang, China and reveal the relationships between the incidence of HFMD and meteorological factors in Xinjiang. With the national surveillance data of HFMD in Xinjiang and meteorological parameters in the study area from 2008 to 2016, in GeoDetector Model, we examined the effects of meteorological factors on the incidence of HFMD in Xinjiang, China, tested the spatial-temporal heterogeneity of HFMD risk, and explored the temporal-spatial patterns of HFMD through the spatial autocorrelation analysis. From 2008 to 2016, the HFMD distribution showed a distinct seasonal pattern and HFMD cases typically occurred from May to July and peaked in June in Xinjiang. Relative humidity, precipitation, barometric pressure and temperature had the more significant influences on the incidence of HFMD than other meteorological factors with the explanatory power of 0.30, 0.29, 0.29 and 0.21 (P<0.000). The interaction between any two meteorological factors had a nonlinear enhancement effect on the risk of HFMD. The relative risk in Northern Xinjiang was higher than that in Southern Xinjiang. Global spatial autocorrelation analysis results indicated a fluctuating trend over these years: the positive spatial dependency on the incidence of HFMD in 2008, 2010, 2012, 2014 and 2015, the negative spatial autocorrelation in 2009 and a random distribution pattern in 2011, 2013 and 2016. Our findings revealed the correlation between meteorological factors and the incidence of HFMD in Xinjiang. The correlation showed obvious spatiotemporal heterogeneity. The study provides the basis for the government to control HFMD based on meteorological information. The risk of HFMD can be predicted with appropriate meteorological factors for HFMD prevention and control.

Spatial Autocorrelation Analysis Using MIG-seq Data Indirectly Estimated the Gamete and Larval Dispersal Range of the Blue Coral, Heliopora coerulea, Within Reefs

Spatial autocorrelation analysis is a well-established technique for detecting spatial structures and patterns in ecology. However, compared to inter-population genetic structure, much less studies examined spatial genetic structure (SGS) within a population by means of spatial autocorrelation analysis. More SGS analysis that compares the robustness of genome-wide single nucleotide polymorphisms (SNPs) and traditional population genetic markers in detecting SGS, and direct comparison between the estimated dispersal range based on SGS and the larval dispersal range of corals directly surveyed in the field would be important. In this study, we examined the SGS of a reef-building coral species, Heliopora coerulea, in two different reefs (Shiraho and Akaishi) using genome-wide SNPs derived from Multiplexed inter-simple sequence repeat (ISSR) genotyping by sequencing (MIG-seq) analysis and nine microsatellite loci for comparison. Microsatellite data failed to reveal significant spatial patterns when using the same number of samples as MIG-seq, whereas MIG-seq analysis revealed significant spatial autocorrelation patterns up to 750 m in both Shiraho and Akaishi reefs based on the maximum significant distance method. However, detailed spatial genetic analysis using fine-scale distance classes (25–200 m) found an x-intercept of 255–392 m in Shiraho and that of 258–330 m in Akaishi reef. The latter results agreed well with a previously reported direct field observation of larval dispersal, indicating that the larvae of H. coerulea settled within a 350 m range in Shiraho reef within one generation. Overall, our results empirically demonstrate that the x-intercept of the spatial correlogram agrees well with the larval dispersal distance that is most frequently found in field observations, and they would be useful for deciding effective conservation management units for maintenance and/or recovery within an ecological time scale.

DETECTION OF THE SPATIAL VARIATIONS OF LOCAL POPULATIONS FROM THE VIEWPOINT OF URBAN STRUCTURE ANALYSIS

In Japan, population decline is one of the important issues that need to be tackled in socioeconomic fields. We apply an analysis method composed of a spatial autocorrelation analysis to the local population data generated through the 1995 and 2015 national census, and try to detect their spatial dynamics in this study. Through making experimental verification of the distance parameter of the spatial autocorrelation analysis, we newly define 2 indices with respect to the size and area where lower local populations are distributed in urbanized areas. It is shown that the local population dynamics are described by the difference of the 2 indices among 1995 and 2015. The expansion of areas where local population decline occurred seems to be detected by our proposed approach.