Impacts of changing scale on Getis-Ord Gi* hotspots of CPUE:a case study of the neon flying squid (Ommastrephes bartramii)in the northwest Pacific Ocean

We examined the scale impacts on spatial hot and cold spots of CPUE for Ommastrephes bartramii in the northwest Pacific Ocean. The original fishery data were tessellated to 18 spatial scales from 5′×5′ to 90′×90′ with a scale interval of 5′ to identify the local clusters. The changes in location, boundaries, and statistics regarding the Getis-Ord Gi＊ hot and cold spots in response to the spatial scales were analyzed in detail. Several statistics including Min, mean, Max, SD, CV, skewness, kurtosis, first quartile（Q1）, median, third quartile（Q3）, area and centroid were calculated for spatial hot and cold spots. Scaling impacts were examined for the selected statistics using linear, logarithmic, exponential, power law and polynomial functions. Clear scaling relations were identified for Max, SD and kurtosis for both hot and cold spots. For the remaining statistics, either a difference of scale impacts was found between the two clusters, or no clear scaling relation was identified. Spatial scales coarser than 30′ are not recommended to identify the local spatial patterns of fisheries because the boundary and locations of hot and cold spots at a coarser scale are significantly different from those at the original scale.

Spatiotemporal analysis,hotspot mapping,and clustering of confirmed cases of COVID-19 in the initial phase of the pandemic in Qom province,Iran

Objective:To identify the incidence rate,relative risk,hotspot regions and incidence trend of COVID-19 in Qom province,northwest part of Iran in the first stage of the pandemic.Methods:The study included 1125 officially reported PCR-confirmed cases of COVID-19 from 20 February 2020 to 20 April 2020 in 90 regions in Qom city,Iran.The Bayesian hierarchical spatial model was used to model the relative risk of COVID-19 in Qom city,and the segmented regression model was used to estimate the trend of COVID-19 incidence rate.The Poisson distribution was applied for the observed number of COVID-19,and independent Gamma prior was used for inference on log-relative risk parameters of the model.Results:The total incidence rate of COVID-19 was estimated at 89.5 per 100000 persons in Qom city(95%CI:84.3,95.1).According to the results of the Bayesian hierarchical spatial model and posterior probabilities,43.33%of the regions in Qom city have relative risk greater than 1;however,only 11.11%of them were significantly greater than 1.Based on Geographic Information Systems(GIS)spatial analysis,10 spatial clusters were detected as active and emerging hotspot areas in the south and central parts of the city.The downward trend was estimated 10 days after the reporting of the first case(February 7,2020);however,the incidence rate was decreased by an average of 4.24%per day(95%CI:−10.7,−3.5).Conclusions:Spatial clusters with high incidence rates of COVID-19 in Qom city were in the south and central regions due to the high population density.The GIS could depict the spatial hotspot clusters of COVID-19 for timely surveillance and decision-making as a way to contain the disease.

Evaluation and Hotspots Identification of Shallow Groundwater Contamination Risk in the Lower Reaches of the Liaohe River Plain

As a prerequisite for groundwater protection and contamination control, evaluation of groundwater con- tamination risk was the extension of groundwater vulnerability assessment. Based on disaster theory and using shallow groundwater of the lower reaches of Liaohe River Plain as the study area, we built an evaluation index system and a contamination index model for groundwater contamination risks from the perspectives of intrinsic vulnerability, external stresses, and functional value. We used data acquisition technology （remote sensing） and spatial analysis technology （GIS） to calculate the value of groundwater contamination risks. The spatial distribution of hotspots was obtained by calculating G index. Results show that groundwater contamination is above a mid-level risk in most of the study area. Areas with extreme high risk account for 37.86%, areas with high risk 32.47%, areas with moderate risk 12.07%, areas with light risk 3.17%, and areas with slight risk 14.43%. Hotspots areas are mainly located in central Shenyang City, northwest of Xinmin City, Beizhen City and Liaozhong County. Coldspots are mainly in Panjin City, Yingkou City, Dashiqiao City, Dawa County and Panshan County. The results reflect the spatial distribution and mechanism of groundwater contamination risk in the study area and provide relative references for land use planning and groundwater resource protection in the lower reaches of the Liaohe River Plain.