scholarly journals Mapping and Spatial Pattern Analysis of COVID-19 in Central Iran Using the Local Indicators of Spatial Association (LISA)

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nahid Jesri ◽  
Abedin Saghafipour ◽  
Alireza Koohpaei ◽  
Babak Farzinnia ◽  
Moharram Karami Jooshin ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Urban Areas ◽  
Analytical Study ◽  
Spatial Association ◽  
Spatial Proximity ◽  
Spatial Pattern Analysis ◽  
Urban District ◽  
Public Health Policies ◽  
High Category ◽  
Urban Districts

Abstract Background Using geographical analysis to identify geographical factors related to the prevalence of COVID-19 infection can affect public health policies aiming at controlling the virus. This study aimed to determine the spatial analysis of COVID-19 in Qom Province, using the local indicators of spatial association (LISA). Methods In a primary descriptive-analytical study, all individuals infected with COVID-19 in Qom Province from February 19th, 2020 to September 30th, 2020 were identified and included in the study. The spatial distribution in urban areas was determined using the Moran coefficient in geographic information systems (GIS); in addition, the spatial autocorrelation of the coronavirus in different urban districts of the province was calculated using the LISA method. Results The prevalence of COVID-19 in Qom Province was estimated to be 356.75 per 100,000 populations. The pattern of spatial distribution of the prevalence of COVID-19 in Qom was clustered. District 3 (Imam Khomeini St.) and District 6 (Imamzadeh Ebrahim St.) were set in the High-High category of LISA: a high-value area surrounded by high-value areas as the two foci of COVID-19 in Qom Province. District 1 (Bajak) of urban districts was set in the Low-High category: a low-value area surrounded by high values. This district is located in a low-value area surrounded by high values. Conclusions According to the results, district 3 (Imam Khomeini St.) and district 6 (Imamzadeh Ebrahim St.) areas are key areas for preventing and controlling interventional measures. In addition, considering the location of District 1 (Bajak) as an urban district in the Low-High category surrounded by high values, it seems that distance and spatial proximity play a major role in the spread of the disease.

scholarly journals Mapping and Spatial Pattern Analysis of COVID -19 in Central Iran Using the Local Indicators of Spatial Association (LISA)

2021 ◽  
Author(s):  
Nahid Jesri ◽  
Abedin Saghafipour ◽  
Alireza Koopaei ◽  
Babak Farzinnia ◽  
Moharram Karami Jooshin ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Urban Areas ◽  
Spatial Association ◽  
Distribution Model ◽  
Spatial Proximity ◽  
Spatial Pattern Analysis ◽  
Policy Makers ◽  
Factors Affecting ◽  
Urban Districts ◽  
Geographical Factors

Abstract Background: Using geographical analysis to identify geographical factors affecting the prevalence of COVID-19 infection can effect on public health policies to control of the virus. The aim of this study was to determine the spatial analysis of COVID-19 regions in Qom Province, using the local indicators of spatial association (LISA). In a descriptive-analytical study, the total number of individuals infected with COVID-19 in Qom Province, from February 19, to September 2020, were included in the study. The spatial distribution in urban areas was determined using the Moran coefficient in GIS. in addition, the spatial autocorrelation of the coronavirus in the different urban districts of the province was calculated using LISA method. Results: The prevalence of COVID -19 in Qom province was estimated to be 356.75 per 100,000 populations. The pattern of spatial distribution of the prevalence of COVID -19 in Qom was clustered. The District 3 (Imam Khomeini St.), and District 6 (Imamzadeh Ebrahim St.), were set in HH category of LISA as two foci of COVID-19 in Qom province. Conclusions: Based on LISA, District 1 (Bajak) of urban districts was set in LH category. It means this district is located in a low value area surrounded by high values. One of the most important geographical factors affecting the incidence of coronavirus is based on spatial distribution model, distance and spatial proximity. So, health policy makers, should impose more restrictions on the observance of health protocols to control of the coronavirus.

scholarly journals Assessing the Distribution of Commuting Trips and Jobs-Housing Balance Using Smart Card Data: A Case Study of Nanjing, China

2019 ◽  
Vol 11 (19) ◽  
pp. 5346 ◽  
Author(s):  
Meina Zheng ◽  
Feng Liu ◽  
Xiucheng Guo ◽  
Xinyue Lei
Keyword(s):  
Smart Card ◽  
Gini Coefficient ◽  
Urban Areas ◽  
Working Population ◽  
Urban District ◽  
Short Supply ◽  
Urban Districts ◽  
Smart Card Data ◽  
Commuting Time ◽  
The Lorenz Curve

The purpose of this research is to assess the distribution of commuting trips and the level of jobs-housing balance with Nanjing smart card data. A new approach is presented using the Lorenz curve and Gini coefficient based on the commuting time. This article also quantifies and visualizes Nanjing’s jobs-housing balance in each urban, suburban and exurban district. The core findings from this research are summarized as follows. First, the Gini coefficient of commuting time is 0.251 in urban areas, 0.258 for suburban areas and 0.267 for exurban areas. The gap of each non-urban district in commuting time is larger than urban districts. Second, the result of jobs-housing ratio (JHR) shows that jobs of Xuanwu district are far more than the working population of this district, whereas jobs and working population in other urban districts are relatively matched. The value of JHR is less than 0.8 in all suburban and exurban districts but Yuhuatai district, which suggests that jobs in these suburban districts (excluding Yuhuatai district) are in short supply compared with their working population. Third, the JHR within a particular district may be different according to the specific locations, especially those areas close to the boundary between two different kinds of districts.

scholarly journals Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China

2021 ◽  
Vol 13 (2) ◽  
pp. 284
Author(s):  
Dan Lu ◽  
Yahui Wang ◽  
Qingyuan Yang ◽  
Kangchuan Su ◽  
Haozhe Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Relative Error ◽  
Urban Areas ◽  
Large Scale ◽  
Population Distribution ◽  
Spatial Optimization ◽  
Distribution Data ◽  
Mountainous Areas ◽  
Mean Relative Error ◽  
Nighttime Light

The sustained growth of non-farm wages has led to large-scale migration of rural population to cities in China, especially in mountainous areas. It is of great significance to study the spatial and temporal pattern of population migration mentioned above for guiding population spatial optimization and the effective supply of public services in the mountainous areas. Here, we determined the spatiotemporal evolution of population in the Chongqing municipality of China from 2000–2018 by employing multi-period spatial distribution data, including nighttime light (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). There was a power function relationship between the two datasets at the pixel scale, with a mean relative error of NTL integration of 8.19%, 4.78% less than achieved by a previous study at the provincial scale. The spatial simulations of population distribution achieved a mean relative error of 26.98%, improved the simulation accuracy for mountainous population by nearly 20% and confirmed the feasibility of this method in Chongqing. During the study period, the spatial distribution of Chongqing’s population has increased in the west and decreased in the east, while also increased in low-altitude areas and decreased in medium-high altitude areas. Population agglomeration was common in all of districts and counties and the population density of central urban areas and its surrounding areas significantly increased, while that of non-urban areas such as northeast Chongqing significantly decreased.

scholarly journals Forest Land Quality Evaluation and the Protection Zoning of Subtropical Humid Evergreen Broadleaf Forest Region Based on the PSO-TOPSIS Model and the Local Indicator of Spatial Association: A Case Study of Hefeng County, Hubei Province, China

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 325
Author(s):  
Li Wang ◽  
Yong Zhou ◽  
Qing Li ◽  
Qian Zuo ◽  
Haoran Gao ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Quality Evaluation ◽  
Spatial Association ◽  
Forest Land ◽  
Mountain Regions ◽  
Land Quality ◽  
Land Protection ◽  
Protection Zones ◽  
Forest Region ◽  
Topsis Model

Forest land is the carrier for growing forests. It is of great significance to evaluate the forest land quality scientifically and delineate forestland protection zones reasonably for realizing better forest land management, promoting ecological civilization construction, and coping with global climate change. In this study, taking Hefeng County, Hubei Province, a subtropical humid evergreen broad-leaved forest region in China, as the study area, 14 indicators were selected from four dimensions—climatic conditions, terrain, soil conditions, and socioeconomics—to construct a forest land quality evaluation index system. Based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model, we introduced the Particle Swarm Optimization (PSO) algorithm to design the evaluation model to evaluate the forest land quality and analyze the distribution of forest land quality in Hefeng. Further, we used the Local Indicator of Spatial Association (LISA) to explore the spatial distribution of forest land quality and delineate the forest land protection zones. The results showed the following: (1) the overall quality of forest land was high, with some variability between regions. The range of Forest Land Quality Index (FLQI) in Hefeng was 0.4091–0.8601, with a mean value of 0.6337. The forest land quality grades were mainly first and second grade, with the higher-grade forest land mainly distributed in the central and southeastern low mountain regions of Zouma, Wuli, and Yanzi. The lower-grade forest land was mainly distributed in the northwestern middle and high mountain regions of Zhongying, Taiping, and Rongmei. (2) The global spatial autocorrelation index of forest land quality in Hefeng County was 0.7562, indicating that the forest land quality in the county had a strong spatial similarity. The spatial distribution of similarity types high-high (HH) and low-low (LL) was more clustered, while the spatial distribution of dissimilarity types high-low (HL) and low-high (LH) was generally dispersed. (3) Based on the LISA of forest land quality, forest land protection zones were divided into three types: key protection zones (KPZs), active protection zones (APZs), and general protection zones (GPZs). The forest land protection zoning basically coincided with the forest land quality. Combining the characteristics of self-correlated types in different forestland protection zones, corresponding management and protection measures were proposed. This showed that the PSO-TOPSIS model can be effectively used for forest land quality evaluation. At the same time, the spatial attributes of forest land were incorporated into the development of forest land protection zoning scheme, which expands the method of forest land protection zoning, and can provide a scientific basis and methodological reference for the reasonable formulation of forest land use planning in Hefeng County, while also serving as a reference for similar regions and countries.

scholarly journals Ecological Connectivity in Agricultural Green Infrastructure:  Suggested Criteria for Fine Scale Assessment and Planning

Land ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 807
Author(s):  
Simone Valeri ◽  
Laura Zavattero ◽  
Giulia Capotorti
Keyword(s):  
Land Cover ◽  
Green Infrastructure ◽  
Rural Areas ◽  
Urban Areas ◽  
Landscape Heterogeneity ◽  
Agricultural Landscapes ◽  
Spatial Pattern Analysis ◽  
Fine Scale ◽  
Unique Type

In promoting biodiversity conservation and ecosystem service capacity, landscape connectivity is considered a critical feature to counteract the negative effects of fragmentation. Under a Green Infrastructure (GI) perspective, this is especially true in rural and peri-urban areas where a high degree of connectivity may be associated with the enhancement of agriculture multifunctionality and sustainability. With respect to GI planning and connectivity assessment, the role of dispersal traits of tree species is gaining increasing attention. However, little evidence is available on how to select plant species to be primarily favored, as well as on the role of landscape heterogeneity and habitat quality in driving the dispersal success. The present work is aimed at suggesting a methodological approach for addressing these knowledge gaps, at fine scales and for peri-urban agricultural landscapes, by means of a case study in the Metropolitan City of Rome. The study area was stratified into Environmental Units, each supporting a unique type of Potential Natural Vegetation (PNV), and a multi-step procedure was designed for setting priorities aimed at enhancing connectivity. First, GI components were defined based on the selection of the target species to be supported, on a fine scale land cover mapping and on the assessment of land cover type naturalness. Second, the study area was characterized by a Morphological Spatial Pattern Analysis (MSPA) and connectivity was assessed by Number of Components (NC) and functional connectivity metrics. Third, conservation and restoration measures have been prioritized and statistically validated. Notwithstanding the recognized limits, the approach proved to be functional in the considered context and at the adopted level of detail. Therefore, it could give useful methodological hints for the requalification of transitional urban–rural areas and for the achievement of related sustainable development goals in metropolitan regions.

scholarly journals Evaluation of Community Resilience in Rural China—Taking Licheng Subdistrict, Guangzhou as an Example

2021 ◽  
Vol 18 (11) ◽  
pp. 5827
Author(s):  
Jianhong Fan ◽  
You Mo ◽  
Yunnan Cai ◽  
Yabo Zhao ◽  
Dongchen Su
Keyword(s):  
Spatial Distribution ◽  
Rural Communities ◽  
Rural Areas ◽  
Urban Areas ◽  
Rural China ◽  
Social Resilience ◽  
Learning Abilities ◽  
The Sustainable Development ◽  
Guangzhou City ◽  
Local Sustainability

Resilience of rural communities is becoming increasingly important to contemporary society. In this study we used a quantitative method to measure the resilience regulating ability of rural communities close to urban areas—in Licheng Subdistrict, Guangzhou City, China. The main results are as follows: (1) Rural systems close to urban areas display superior adapting and learning abilities and have a stronger overall resilience strength, the spatial distribution of which is characterized by dispersion in whole and aggregation in part; (2) the resilience of most rural economic subsystems can reach moderate or higher levels with apparent spatial agglomeration, whilst the ecological subsystem resilience and social resilience are generally weaker; the spatial distribution of the former shows a greater regional difference while the latter is in a layered layout; (3) some strategies such as rebuilding a stable ecological pattern, making use of urban resources and cultivating rural subjectivity are proposed on this basis, in order to promote the sustainable development of rural areas and realize rural revitalization. This work also gives suggestion for the creation of appropriate and effective resilience standards specifically targeted for rural community-aiming to achieve the delivery of local sustainability goals.

scholarly journals Seasonal variation and spatial distribution of carbonaceous aerosols in Taiwan

2010 ◽  
Vol 10 (19) ◽  
pp. 9563-9578 ◽  
Author(s):  
C. C.-K. Chou ◽  
C. T. Lee ◽  
M. T. Cheng ◽  
C. S. Yuan ◽  
S. J. Chen ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Rural Areas ◽  
Urban Areas ◽  
Air Pollutants ◽  
Chemical Properties ◽  
Anthropogenic Sources ◽  
Carbonaceous Aerosols ◽  
Urban Rural ◽  
Southern Taiwan ◽  
Northern Taiwan

Abstract. To investigate the physico-chemical properties of aerosols in Taiwan, an observation network was initiated in 2003. In this work, the measurements of the mass concentration and carbonaceous composition of PM10 and PM2.5 are presented. Analysis on the data collected in the first 5-years, from 2003 to 2007, showed that there was a very strong contrast in the aerosol concentration and composition between the rural and the urban/suburban stations. The five-year means of EC at the respective stations ranged from 0.9±0.04 to 4.2±0.1 μgC m−3. In rural areas, EC accounted for 2–3% of PM10 and 3–5% of PM2.5 mass loadings, comparing to 4–6% of PM10 and 4–8% of PM2.5 in the urban areas. It was found that the spatial distribution of EC was consistent with CO and NOx across the network stations, suggesting that the levels of EC over Taiwan were dominated by local sources. The measured OC was split into POC and SOC counterparts following the EC tracer method. Five-year means of POC ranged from 1.8±0.1 to 9.7±0.2 μgC m−3 among the stations. It was estimated that the POM contributed 5–17% of PM10 and 7–18% of PM2.5 in Taiwan. On the other hand, the five-year means of SOC ranged from 1.5±0.1 to 3.8±.3 μgC m−3. The mass fractions of SOM were estimated to be 9–19% in PM10 and 14–22% in PM2.5. The results showed that the SOC did not exhibit significant urban-rural contrast as did the POC and EC. A significant cross-station correlation between SOC and total oxidant was observed, which means the spatial distribution of SOC in Taiwan was dominated by the oxidant mixing ratio. Besides, correlation was also found between SOC and particulate nitrate, implying that the precursors of SOA were mainly from local anthropogenic sources. In addition to the spatial distribution, the carbonaceous aerosols also exhibited distinct seasonality. In northern Taiwan, the concentrations of all the three carbonaceous components (EC, POC, and SOC) reached their respective minima in the fall season. POC and EC increased drastically in winter and peaked in spring, whereas the SOC was characterized by a bimodal pattern with the maximal concentration in winter and a second mode in summertime. In southern Taiwan, minimal levels of POC and EC occurred consistently in summer and the maxima were observed in winter, whereas the SOC peaked in summer and declined in wintertime. The discrepancies in the seasonality of carbonaceous aerosols between northern and southern Taiwan were most likely caused by the seasonal meteorological settings that dominated the dispersion of air pollutants. Moreover, it was inferred that the Asian pollution outbreaks could have shifted the seasonal maxima of air pollutants from winter to spring in the northern Taiwan, and that the increases in biogenic SOA precursors and the enhancement in SOA yield were responsible for the elevated SOC concentrations in summer.

scholarly journals Cities, the Urban Green Environment, and Individual Subjective Well-Being: The Case of Milan, Italy

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Giorgio Tavano Blessi ◽  
Enzo Grossi ◽  
Giovanni Pieretti ◽  
Guido Ferilli ◽  
Alessandra Landi
Keyword(s):  
Urban Areas ◽  
Well Being ◽  
Spatial Proximity ◽  
Independent Effect ◽  
Subjective Well Being ◽  
Residential Proximity ◽  
Urban Green ◽  
Urban Green Areas ◽  
Green Areas ◽  
The Individual

This paper evaluates the independent effect of the spatial proximity of green urban areas upon the individual subjective well-being of the Milan population (Italy). The methodology is based on a survey undertaken in 2010 using a sample of 1,000 of Milan citizens. Univariate and multivariate analyses and GIS localization have been employed in order to rank the major individual well-being determinants and the relationship between citizens and urban green areas. Results show that the residential proximity of citizens to urban green areas seems to have little bearing on individual subjective well-being.

scholarly journals Spatial analysis of deaths from pulmonary tuberculosis in the city of São Luís, Brazil

2014 ◽  
Vol 40 (5) ◽  
pp. 543-551 ◽  
Author(s):  
Marcelino Santos-Neto ◽  
Mellina Yamamura ◽  
Maria Concebida da Cunha Garcia ◽  
Marcela Paschoal Popolin ◽  
Tatiane Ramos dos Santos Silveira ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Pulmonary Tuberculosis ◽  
Secondary Data ◽  
Positive Association ◽  
Significant Positive Association ◽  
Public Health Policies ◽  
Operational Characteristics ◽  
Operational Variables ◽  
The City ◽  
Selection Of

OBJECTIVE: To characterize deaths from pulmonary tuberculosis, according to sociodemographic and operational variables, in the city of São Luís, Brazil, and to describe their spatial distribution. METHODS: This was an exploratory ecological study based on secondary data from death certificates, obtained from the Brazilian Mortality Database, related to deaths from pulmonary tuberculosis. We included all deaths attributed to pulmonary tuberculosis that occurred in the urban area of São Luís between 2008 and 2012. We performed univariate and bivariate analyses of the sociodemographic and operational variables of the deaths investigated, as well as evaluating the spatial distribution of the events by kernel density estimation. RESULTS: During the study period, there were 193 deaths from pulmonary tuberculosis in São Luís. The median age of the affected individuals was 52 years. Of the 193 individuals who died, 142 (73.60%) were male, 133 (68.91%) were Mulatto, 102 (53.13%) were single, and 64 (33.16%) had completed middle school. There was a significant positive association between not having received medical care prior to death and an autopsy having been performed (p = 0.001). A thematic map by density of points showed that the spatial distribution of those deaths was heterogeneous and that the density was as high as 8.12 deaths/km2. CONCLUSIONS: The sociodemographic and operational characteristics of the deaths from pulmonary tuberculosis evaluated in this study, as well as the identification of priority areas for control and surveillance of the disease, could promote public health policies aimed at reducing health inequities, allowing the optimization of resources, as well as informing decisions regarding the selection of strategies and specific interventions targeting the most vulnerable populations.

scholarly journals Men's health: non-communicable chronic diseases and social vulnerability

2016 ◽  
Vol 24 (0) ◽  
Author(s):  
Daniele Natália Pacharone Bertolini Bidinotto ◽  
Janete Pessuto Simonetti ◽  
Silvia Cristina Mangini Bocchi
Keyword(s):  
Spatial Distribution ◽  
Chronic Diseases ◽  
Glucose Intolerance ◽  
Social Vulnerability ◽  
Medical Records ◽  
Urban Areas ◽  
International Classification Of Diseases ◽  
Classification Of Diseases ◽  
Mixed Approach ◽  
The Relationship

ABSTRACT Objectives: to evaluate the relationship between absences in scheduled appointments and the number of non-communicable chronic diseases and to investigate the relationship between spatial distribution of these diseases and social vulnerability, using geoprocessing. Method: a quantitative study of sequential mixed approach by analyzing 158 medical records of male users to relate the absences and 1250 medical records for geoprocessing Results: the higher the number of absences in the scheduled medical appointments, the less were the number of non-communicable chronic diseases and the ones listed in the International Classification of Diseases in single men. There were 21 significant geostatistically cases of glucose intolerance in the urban area. Of these, 62% lived in a region with a social vulnerability rating of Very Low, Medium 19%, 14% Low and 5% High. Conclusion: it was observed that the older the men, the greater is the number of chronic diseases and the less they miss scheduled appointments. Regarding the use of geoprocessing, we obtained a significant number of cases of glucose intolerance in urban areas, the majority classified as Very Low social vulnerability. It was possible to relate the spatial distribution of these diseases with the social vulnerability classification; however, it was not possible to perceive a relationship of them with the higher rates of social vulnerability.

Sign in / Sign up

Export Citation Format

Share Document