Pneumonia incidence and determinants in South Punjab, Pakistan (2016–2020): a spatial epidemiological study at Tehsil-level

LTPP data-based investigation on asphalt pavement performance using geospatial hot spot analysis and decision tree models

This study evaluates the influences of air pollution in China using a recently proposed model—multi‐scale geographically weighted regression (MGWR). First, we review previous research on the determinants of air quality. Then, we explain the MGWR model, together with two global models: ordinary least squares (OLS) and OLS containing a spatial lag variable (OLSL) and a commonly used local model: geographically weighted regression (GWR). To detect and account for any variation of the spatial autocorrelation of air pollution over space, we construct two extra local models which we call GWR with lagged dependent variable (GWRL) and MGWR with lagged dependent variable (MGWRL) by including the lagged form of the dependent variable in the GWR model and the MGWR model, respectively. The performances of these six models are comprehensively examined and the MGWR and MGWRL models outperform the two global models as well as the GWR and GWRL models. MGWRL is the most accurate model in terms of replicating the observed air quality index (AQI) values and removing residual dependency. The superiority of the MGWR framework over the GWR framework is demonstrated—GWR can only produce a single optimized bandwidth, while MGWR provides covariate‐specific optimized bandwidths which indicate the different spatial scales that different processes operate.

Infectious diarrhea cases have increased during the past years in the Anhui Province of China, but little is known about its spatial cluster pattern and associated socioeconomic factors. We obtained county-level total cases of infectious diarrhea in 105 counties of Anhui in 2016 and computed age-adjusted rates. Socioeconomic factors were collected from the Statistical Yearbook. Hot spot analysis was used to identify hot and cold spot counties for infectious diarrhea incidence. We then applied binary logistic regression models to determine the association between socioeconomic factors and hot spot or cold spot clustering risk. Hot spot analysis indicated there were both significant hot spot (29 counties) and cold spot (18 counties) clustering areas for infectious diarrhea in Anhui (P < 0.10). Multivariate binary logistic regression results showed that infectious diarrhea hot spots were positively associated with per capita gross domestic product (GDP), with an adjusted odds ratio (AOR): 3.51, 95% CI: 2.09-5.91, whereas cold spots clustering were positively associated with the number of medical staffs (AOR: 1.18, 95% CI: 1.08-1.29) and negatively associated with the number of public health physicians (AOR: 0.27, 95% CI: 0.09-0.86). We identified locations for hot and cold spot clusters of infectious diarrhea incidence in Anhui, and the clustering risks were significantly associated with health workforce resources and the regional economic development. Targeted interventions should be carried out with considerations of regional socioeconomic conditions.

Understanding the spatial distribution of land surface temperature (LST) and its impact factors is crucial for mitigating urban heat island effect. However, few studies have quantitatively investigated the spatial non-stationarity and spatial scale effects of the relationships between LST and its impact factors at multi-scales. The main purposes of this study are as follows: (1) to estimate the spatial distributions of urban heat island (UHI) intensity by using hot spots analysis and (2) to explore the spatial non-stationarity and scale effects of the relationships between LST and related impact factors at multiple resolutions (30–1200 m) and to find appropriate scales for illuminating the relationships in a plain city. Based on the LST retrieved from Landsat 8 OLI/TIRS images, the Geographically-Weighted Regression (GWR) model is used to explore the scale effects of the relationships in Zhengzhou City between LST and six driving indicators: The Fractional Vegetation Cover (FVC), the Impervious Surface (IS), the Population Density (PD), the Fossil-fuel CO2 Emission data (FFCOE), the Shannon Diversity Index (SHDI) and the Perimeter-area Fractal Dimension (PAFRAC),which indicate the vegetation abundance, built-up, social-ecological variables and the diversity and shape complexity of land cover types. Our findings showed that the spatial patterns of LST show statistically significant hot spot zones in the center of the study area, partly extending to the western and southern industrial areas, indicating that the intensity of the urban heat island is significantly spatial clustering in Zhengzhou City. In addition, compared with the Ordinary Least Squares (OLS) model, the GWR model has a better ability to characterize spatial non-stationarity and analyze the relationships between the LST and its impact factors by considering the space-varying relationships of different variables, especially at the fine spatial scales (30–480 m). However, the strength of GWR model has become relatively weak with the increase of spatial scales (720–1200 m). This reveals that the GWR model is recommended to be applied in the analysis of UHI problems and related impact factors at scales finer than 480 m in the plain city. If the spatial scale is coarser than 720 m, both OLS and GWR models are suitable for illustrating the correct relationships between UHI effect and its influence factors in the plain city due to their undifferentiated performance. These findings can provide valuable information for urban planners and researchers to select appropriate models and spatial scales seeking to mitigate urban thermal environment effect.

Monitoring spatial changes of surface heat island formation and temperature changes in sub-urban areas is vital in the freshwater lake management of urban areas as frequent phenomena related to climate change have undergone. The purpose of this study was to examine the Spatio-temporal pattern of urban heat island and land surface temperature and vegetation changes by using GI statistics, where hotspot analysis was also performed. The study further examined the effect of heat island and surface temperature on urban freshwater lakes where hot and cold spots identified had undergone a reclassification process. The results revealed that the increasing Land Surface Temperature (LST) due to modification and transformation of vegetated areas into concrete and synthetic built-up extents is one of the challenging problems in the selected suburbs. Both NDVI and LST hot spots and cold spots have changed compared to 2010. The LST showed considerable expansion of the hotspots within ten years rather than cold spots in all three suburbs. The freshwater lakes are in proximity to the city. All three lakes were finally reclassified as hotspot areas for LST, while Kesbewa Lake and Thalangama Lake were identified as NDVI hotspots where the vegetation cover had contracted by 2020. Even though Boralesgamuwa Lake is not recognized as an NDVI hotspot, the encroachment and expansion of the current hotspot area could be identified. The study's findings could be used to design sustainable cities in these suburbs more by prioritizing the conservation of urban ecosystems.

Monitoring spatial changes of surface heat island formation and temperature changes in sub-urban areas is vital in the freshwater lake management of urban areas as frequent phenomena related to climate change have undergone. The purpose of this study was to examine the Spatio-temporal pattern of urban heat island and land surface temperature and vegetation changes by using GI statistics, where hotspot analysis was also performed. The study further examined the effect of heat island and surface temperature on urban freshwater lakes where hot and cold spots identified had undergone a reclassification process. The results revealed that the increasing Land Surface Temperature (LST) due to modification and transformation of vegetated areas into concrete and synthetic built-up extents is one of the challenging problems in the selected suburbs. Both NDVI and LST hot spots and cold spots have changed compared to 2010. The LST showed considerable expansion of the hotspots within ten years rather than cold spots in all three suburbs. The freshwater lakes are in proximity to the city. All three lakes were finally reclassified as hotspot areas for LST, while Kesbewa Lake and Thalangama Lake were identified as NDVI hotspots where the vegetation cover had contracted by 2020. Even though Boralesgamuwa Lake is not recognized as an NDVI hotspot, the encroachment and expansion of the current hotspot area could be identified. The study's findings could be used to design sustainable cities in these suburbs more by prioritizing the conservation of urban ecosystems.

Identification of the co-existence of low total organic carbon contents and low pH values in agricultural soil in north-central Europe using hot spot analysis based on GEMAS project data

Land urbanization is a comprehensive mapping of the relationship between urban production, life and ecology in urban space and a spatial carrier for promoting the modernization of cities. Based on the remote sensing monitoring data of the land use status of the Yangtze River Delta urban agglomeration collected in 2010 and 2020, the spatial differentiation characteristics and influencing factors of land urbanization in the area were analyzed comprehensively using hot spot analysis, kernel density estimation, the multi-scale geographically weighted regression (MGWR) model and other methods. The results indicated the following: (1) From 2010 to 2020, the average annual growth rate of land urbanization in the Yangtze River Delta urban agglomeration was 0.50%, and nearly 64.28% of the counties had an average annual growth rate that lagged behind the overall growth rate. It exhibited dynamic convergence characteristics. (2) The differentiation pattern of land urbanization in the Yangtze River Delta urban agglomeration was obvious from the southeast to the northwest. The hot spots of land urbanization were consistently concentrated in the southeastern coastal areas and showed a trend of spreading, while the cold spots were concentrated in the northwest of Anhui Province, showing a shrinking trend. (3) Compared with the GWR model and the OLS model, the MGWR model has a better fitting effect and is more suitable for studying the influencing factors of land urbanization. In addition, there were significant spatial differences in the scale and degree of influence of different influencing factors. Analyzing and revealing the spatiotemporal characteristics and driving mechanism of land urbanization in the Yangtze River Delta urban agglomeration has important theoretical value and practical significance for the scientific understanding of new-type urbanization and the implementation of regional integration and rural revitalization strategies.

Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar

Abstract. After the bubble economy collapsed at the beginning of the 1990s, the government’s deregulation policies accelerated urban development in the Tokyo Metropolitan Area. This resulted in increased trade in real estate and accelerated population growth in downtown Tokyo. However, that trend was not observed in all areas: instead, it exacerbated the spatial differentiation that was already apparent in the Tokyo Metropolitan Area. Hirayama (2005, 2006, 2011) found that government policy to promote housing supply and increase urban redevelopment split urban space into hot spots, with new investments, and redeveloped districts and cold spots, with stagnant and depopulated districts. However, the precise locations of such spots are not obvious, as those studies did not map them.This study identified and mapped hot and cold spots in Tokyo with the use of spatial analysis with GIS. To this end, we employed grid square population statistics for 1985, 1995, and 2005, which encompasses the entire period of the bubble economy and its aftermath. The analysis of hot spots using the Getis-Ord Gi* statistic was performed on data for population change in 23 wards of Tokyo in this period. Then, we explored the detailed composition of the population and the background of the changes in a consideration of the socio-economic shift of Tokyo during this period.The results of the analysis indicated that hot and cold spots coexisted in central Tokyo, and their spatial distribution changed drastically following the collapse of the bubble economy at the beginning of the 1990s. Between 1985 and 1995, populations show a concentric pattern of change: cold spots are observed in areas close to the city center and hot spots appear on the outskirts of the study area. This pattern is a result of population outflows due to soaring land prices during the period of the bubble economy.However, population changes between 1995 and 2005 indicated a different pattern and the resulting distribution of hot and cold spots was dispersed. Specifically, hot spots appeared in the south and east parts of central Tokyo, where highrise condominiums were being built on the sites of former factories or warehouses in the coastal areas of Tokyo Bay. In these districts, a marked increase of white-collar workers was observed, an indicator of gentrification. By contrast, cold spots are noted on the northern side of central Tokyo, where large public housing estates are located and their population has declined and aged. The contrast between the north and the south of Tokyo became obvious after the bubble economy collapsed.

Geographically Weighted Regression (GWR) has been broadly used in various fields to model spatially non-stationary relationships. Multi-scale Geographically Weighted Regression (MGWR) is a recent advancement to the classic GWR model. MGWR is superior in capturing multi-scale processes over the traditional single-scale GWR model by using different bandwidths for each covariate. However, the multiscale property of MGWR brings additional computation costs. The calibration process of MGWR involves iterative back-fitting under the additive model (AM) framework. Currently, MGWR can only be applied on small datasets within a tolerable time and is prohibitively time-consuming to run with moderately large datasets (greater than 5,000 observations). In this paper, we propose a parallel implementation that has crucial computational improvements to the MGWR calibration. This improved computational method reduces both memory footprint and runtime to allow MGWR modelling to be applied to moderate-to-large datasets (up to 100,000 observations). These improvements are integrated into the mgwr python package and the MGWR 2.0 software, both of which are freely available to download.

Agriculture is the main livelihood and food security source in India's rural Himalayas. At the same time, frequent landslides are increasing the risk of agriculture, particularly under the changing scenario of climate. However, a few studies explored the impact of landslides on the agricultural land in the Himalayan region. Therefore, the study focuses on two-fold objectives: i.e. (i) to analyze the impact of landslides on losing agricultural land, and (ii) to assess the risk of agricultural land to landslides. We consider the Darjeeling Himalayas of India as a case study of this research. The study area is composed of diverse agricultural lands such as tea plantations, pomiculture, and cropland. To achieve these objectives, a detailed landslide inventory database is generated that covers landslides from 2001 to 2024. We develop a GIS-based framework of the risk assessment using five indicators namely the susceptibility index of landslides, temporal probability index of landslides, total affected area index of landslides, proximity index of landslides, and recovery index of agricultural land. The study considers each village as a unit of analysis. Further, a composite risk index was developed by aggregating the five indexes.&amp;#160; Further, the spatial pattern of risk is analyzed using hot spot and cold spot analysis. The study found varying impacts and risks of landslides on tea plantations and frame land. The study will help to develop sustainable agricultural policy in the rural Himalayas.Key words: Landslides; Risk index; Agricultural land; GIS; Hot-spot analysis; Darjeeling

Over the past two decades, extensive monitoring has been conducted in the St. Clair – Detroit River System to describe spatial and temporal patterns of lake sturgeon (Acipenser fulvescens). To characterize spatial patterns in juvenile lake sturgeon (<1000 mm TL) based on survey collections, ‘hot spots’ were identified through optimized hot spot analysis (HSA). This HSA was then interpolated by inverse distance weighted analysis to determine extent of identified ‘hot spots’ and ‘cold spots’. Additionally, habitat variables (i.e., water depth, water velocity, and dominant substrate type) were investigated using a single season occupancy model to determine their influence on juvenile lake sturgeon occupancy probability. In total, 1203 juvenile lake sturgeon were captured across 4197 surveys. Three unique ‘hot spots’ were identified; western Lake Erie, Fighting Island in the Detroit River, and the North Channel in the St. Clair River. Interpolated ‘hot spots’ encompassed 73.1 km² in western Lake Erie, 4.7 km² near Fighting Island, and 6.6 km² in the North Channel. Detection probabilities within ‘hot spots’ ranged from 8.8%–43.4%. No habitat variables significantly predicted juvenile lake sturgeon occupancy. Juvenile lake sturgeon were captured in western Lake Erie where the water depth was >5.1 m and odds of occupancy increased with increased water velocity. Juvenile lake sturgeon in the Detroit and St. Clair River ‘hot spots’ were captured at sites with mean benthic water velocities ranging from 0.20–0.60 m/s and where water depth was >7.3 m. Irrespective of waterbody, 69% of all juveniles were detected over dominant sand and gravel substrates. These results provide valuable insight about juvenile habitat use that can help managers formulate effective conservation and restoration strategies supporting the continued recovery of Great Lakes lake sturgeon.

ABSTRACTRoad traffic crashes (RTCs) are a leading cause of death and disability. In low- and middle-income countries, vulnerable road users are commonly involved in injurious RTCs. This study describes epidemiological and built environment analysis (BEA) of in Galle, Sri Lanka. After ethical and police permission, police data were collected and descriptive statistics tabulated. Spatial analysis identified hot spots and BEA was conducted at each location. Seven hundred and fifty-two victim data from 389 reported RTCs were collected. Most victims were male (91%) 21–50 years of age (>70%). Forty-nine percent of RTCs were non-grievous. Crashes commonly included motorcycles (33.9%), three-wheelers (18.3%) or cars (14.4%). Most victims were drivers (33.4%) or pedestrians (21.3%). Factors contributing to RTCs include aggressive driving (44.5%) or speeding (42.7%). All hotspots were in urban areas, and most were at intersections (63%). Further analysis of hot spots is necessary to identify areas for intervention.

The analysis of hot spots and trends in autism eye movement research is of great significance to the further development of autism research. This paper uses 915 valid documents screened from the Web of Science Core Collection database as a sample, and uses CiteSpace 6.1R6 software to analyze the hot spots and future research trends of autism eye movement research from 2008 to 2023. The study shows that in recent years, the research in this field has generally shown an upward trend, but it has shown a downward trend after reaching a peak in 2021; the cooperation between authors needs to be strengthened; each cluster in this field has a relatively clear research theme, and the keywords contained in the cluster are closely related; the latest research hotspot is artistic features (autistic trait).