Local Indicators Of Spatial Association Research Articles

Breast cancer incidence in Yogyakarta, Indonesia from 2008-2019: A cross-sectional study using trend analysis and geographical information system.

Breast cancer is a significant public health concern worldwide, including in Indonesia. Little is known about the spatial and temporal patterns of breast cancer incidence in Indonesia. This study aimed to analyze temporal and spatial variations of breast cancer incidence in Yogyakarta Province, Indonesia. The study used breast cancer case data from the Yogyakarta Population-Based Cancer Registry (PBCR) from 2008 to 2019. The catchment areas of the PBCR included the 48 subdistricts of 3 districts (Sleman, Yogyakarta City, and Bantul). Age-standardized incidence rates (ASR) were calculated for each subdistrict. Joinpoint regression was used to detect any significant changes in trends over time. Global Moran's and Local Indicators of Spatial Association (LISA) analyses were performed to identify any spatial clusters or outliers. The subdistricts had a median ASR of 41.9, with a range of 15.3-70.4. The majority of cases were diagnosed at a late stage, with Yogyakarta City having the highest proportion of diagnoses at stage 4. The study observed a significant increasing trend in breast cancer incidence over the study period the fastest of which is in Yogyakarta City with an average annual percentage change of 18.77%, with Sleman having an 18.21% and Bantul having 8.94% average changes each year (p <0.05). We also found a significant positive spatial autocorrelation of breast cancer incidence rates in the province (I = 0.581, p <0.001). LISA analysis identified 11 subdistricts which were high-high clusters in the central area of Yogyakarta City and six low-low clusters in the southeast region of the catchment area in the Bantul and Sleman Districts. No spatial outliers were identified. We found significant spatial clustering of BC ASR in the Yogyakarta Province, and there was a trend of increasing ASR across the region. These findings can inform resource allocation for public health efforts to high-risk areas and develop targeted prevention and early detection strategies. Further res is needed to understand the factors driving the observed temporal and spatial patterns of breast cancer incidence in Yogyakarta Province, Indonesia.

Exploring the Spatial Pattern of Urban Forest Ecosystem Services based on i-Tree Eco and Spatial Interpolation: A Case Study of Kyoto City, Japan.

Urban forest, as an essential urban green infrastructure, is critical in providing ecosystem services to cities. To enhance the mainstreaming of ecosystem services in urban planning, it is necessary to explore the spatial pattern of urban forest ecosystem services in cities. This study provides a workflow for urban forest planning based on field investigation, i-Tree Eco, and geostatistical interpolation. Firstly, trees across an array of land use types were investigated using a sampling method. Then i-Tree Eco was applied to quantify ecosystem services and ecosystem service value in each plot. Based on the ecosystem services estimates for plots, four interpolation methods were applied and compared by cross-validation. The Empirical Bayesian Kriging was determined as the best interpolation method with higher prediction accuracy. With the results of Empirical Bayesian Kriging, this study compared urban forest ecosystem services and ecosystem service value across land use types. The spatial correlations between ecosystem service value and four types of point of interest in urban places were explored using the bivariate Moran's I statistic and the bivariate local indicators of spatial association. Our results show that the residential area in the built-up area of Kyoto city had higher species richness, tree density, ecosystem services, and total ecosystem service value. Positive spatial correlations were found between ecosystem service value and the distribution of urban space types including the tourist attraction distribution, urban park distribution, and school distribution. This study provides a specific ecosystem service-oriented reference for urban forest planning based on land use and urban space types.

Exploring regional disparities in lung cancer mortality in a Brazilian state: A cross-sectional ecological study.

Lung cancer (LC) is one of the main causes of mortality in Brazil; geographic, cultural, socioeconomic and health access factors can affect the development of the disease. We explored the geospatial distribution of LC mortality, and associated factors, between 2015 and 2019, in Parana state, Brazil. We obtained mortality (from the Brazilian Health Informatics Department) and population rates (from the Brazilian Institute of Geography and Statistics [IBGE]) in people over 40 years old, accessibility of oncology centers by municipality, disease diagnosis rate (from Brazilian Ministry of Health), the tobacco production rate (IBGE) and Parana Municipal Performance Index (IPDM) (from Parana Institute for Economic and Social Development). Global Moran's Index and Local Indicators of Spatial Association were performed to evaluate the spatial distribution of LC mortality in Parana state. Ordinary Least Squares Regression and Geographically Weighted Regression were used to verify spatial association between LC mortality and socioeconomic indicators and health service coverage. A strong spatial autocorrelation of LC mortality was observed, with the detection of a large cluster of high LC mortality in the South of Parana state. Spatial regression analysis showed that all independent variables analyzed were directly related to LC mortality by municipality in Paraná. There is a disparity in the LC mortality in Parana state, and inequality of socioeconomic and accessibility to health care services could be associated with it. Our findings may help health managers to intensify actions in regions with vulnerability in the detection and treatment of LC.

Epidemiological characteristics and spatiotemporal clustering of scarlet fever in Liaoning Province, China, 2010–2019

BackgroundTo explore the epidemiological characteristics and spatiotemporal distribution of scarlet fever in Liaoning Province, which could provide scientific evidence for the formulation and improvement of prevention and control strategies and measures. MethodsData on scarlet fever cases and population were obtained from the China Information System for Disease Control and Prevention in Liaoning Province between 2010 and 2019. We examined the spatial and spatiotemporal clusters of scarlet fever across Liaoning Province using the Moran's I, local indicators of spatial association, local Gi* hotspot statistics, and Kulldorff's retrospective space-time scan statistical analysis. ResultsBetween 1st January 2010 and 31st December 2019, 46,652 cases of scarlet fever were reported in Liaoning Province, with an annual average incidence of 10.67 per 100,000. The incidence of scarlet fever had obvious seasonality with high incidence in early summer June and early winter December. The male-to-female ratio was 1.53:1. The highest incidence of cases occurred in 3–9 year old children. The most likely spatiotemporal cluster and the secondary clusters were detected in urban regions of Shenyang and Dalian, Liaoning Province. ConclusionsThe incidence of scarlet fever has obvious spatiotemporal clustering, with the high-risk areas mainly concentrated in urban area of Shenyang and Dalian, Liaoning Province. Control strategies need to focus on high-risk season, high-risk areas and high-risk populations in order to reduce the incidence of scarlet fever.

A Decision Support GIS Framework for Establishing Zero-Emission Maritime Networks: The Case of the Greek Coastal Shipping Network

Sustainability of maritime operations is a topic widely considered in recent years, as the shipping industry attempts to limit its environmental impact and meet the decarbonization goals set by the International Maritime Organization (IMO). As alternative fuels and newer ship technologies are gaining interest, the shift to more environmentally friendly fleets is quickly becoming a reality. In this context, potential areas for such shifts need to be determined, to expedite decarbonization efforts and provide passengers with a more sustainable way of travel. Greece is an insular country, with a complex coastal shipping network connecting the mainland with the islands and being of paramount importance for their economic growth. Recognizing accessibility and decarbonization needs, this paper examines whether the Greek coastal shipping network (GCSN) can be restructured, by introducing zero-emission sub-networks operated by electric ferries. The aim is to propose a methodological framework for the spatial analysis and evaluation of coastal networks, with the implementation of exploratory spatial data analysis (ESDA) methods and determination of local indicators of spatial association (LISA) with the help of geographic information systems (GIS). The proposed framework provides insight on whether and where such a restructuring is possible, with the introduction of new transshipment port hubs in the islands from which electric ferries could operate, thus determining potential electrification areas with additionally high renewable resource potential. Final conclusions indicate that a potential electrification of certain parts of the GCSN could be possible, while results for GHG emissions reduced by the introduction of electric ferries are calculated.

Spatial Association Patterns with Cultural and Behaviour with the Situations of COVID-19

This study was a cross-sectional study. The study of spatial association patterns and the influences on the Coronavirus Disease 2019 (COVID-19) epidemic situation in Thailand was performed using secondary data from the COVID-19 interactive dashboard, Department of Disease Control, Ministry of Public Health, between January 1st, 2020, and December 31st, 2021. Moran’s I, Local Indicators of Spatial Association (LISA), and Spatial Regression was applied for statistical analysis. In the epidemic situation of COVID-19, the highest of 11,512.65 per one hundred thousand population, and the spatial association between the nighttime light average, the prevalence of smokers in Thailand, the proportion of population per village health volunteer, and the proportion of population per health care center with the epidemic situation of COVID-19 has Moran’s I = 0.309, 0.396, 0.081 and 0.424, respectively. From the Spatial Lag Model (SLM), a factor that has a spatial association with the epidemic situation of COVID-19 is the nighttime light average, the prevalence of smokers in Thailand, and the proportion of population per healthcare center, which can predict the epidemic situation of COVID-19 by 47.8 percent (R2 =0.478). The growth factor of a large city is an important factor for population density which is a major cause of spread of the coronavirus easily. Moreover, smoking behavior has encouraged the epidemic to spread rapidly. The situation is serious as the number of hospitals is not enough to support the treatment and screening of patients to cover the entire population of Thailand. Therefore, it is urgent that the government plan to mitigate the situation with maximum efficiency by having Covid-19 centers and increase the number of beds and facilities.

Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019.

Chronic respiratory diseases (CRDs) constitute 4% of the global disease burden and cause 4 million deaths annually. This cross-sectional study used QGIS and GeoDa to explore the spatial pattern and heterogeneity of CRDs morbidity and spatial autocorrelation between socio-demographic factors and CRDs in Thailand from 2016 to 2019. We found an annual, positive, spatial autocorrelation (Moran's I >0.66, p<0.001) showing a strong clustered distribution. The local indicators of spatial association (LISA) identified hotspots mostly in the northern region, while coldspots were mostly seen in the central and north-eastern regions throughout the study period. Of the socio-demographic factors, the density of population, households, vehicles, factories and agricultural areas, correlated with the CRD morbidity rate, with statistically significant negative spatial autocorrelations and coldspots in the north-eastern and central areas (except for agricultural land) and two hotspots between farm household density and CRD in the southern region in 2019. This study identified vulnerable provinces with high risk of CRDs and can guide prioritization of resource allocation and provide target interventions for policy makers.

Spatial autocorrelation and heterogenicity of demographic and healthcare factors in the five waves of COVID-19 epidemic in Thailand

A study of 2,569,617 Thailand citizens diagnosed with COVID-19 from January 2020 to March 2022 was conducted with the aim of identifying the spatial distribution pattern of incidence rate of COVID-19 during its five main waves in all 77 provinces of the country. Wave 4 had the highest incidence rate (9,007 cases per 100,000) followed by the Wave 5, with 8,460 cases per 100,000. We also determined the spatial autocorrelation between a set of five demographic and health care factors and the spread of the infection within the provinces using Local Indicators of Spatial Association (LISA) and univariate and bivariate analysis with Moran's I. The spatial autocorrelation between the variables examined and the incidence rates was particularly strong during the waves 3-5. All findings confirmed the existence of spatial autocorrelation and heterogenicity of COVID-19 with the distribution of cases with respect to one or several of the five factors examined. The study identified significant spatial autocorrelation with regard to the COVID-19 incidence rate with these variables in all five waves. Depending on which province that was investigated, strong spatial autocorrelation of the High-High pattern was observed in 3 to 9 clusters and of the Low-Low pattern in 4 to 17 clusters, whereas negative spatial autocorrelation was observed in 1 to 9 clusters of the High-Low pattern and in 1 to 6 clusters of Low-High pattern. These spatial data should support stakeholders and policymakers in their efforts to prevent, control, monitor and evaluate the multidimensional determinants of the COVID-19 pandemic.

Socioeconomic Risk Factors for Pediatric Out-of-hospital Cardiac Arrest: A Statewide Analysis.

Economic hardship is a major threat to children's health, implying that pediatric out-of-hospital cardiac arrest (pOHCA) might be promoted by lower incomes and child poverty. To target resources, it is helpful to identify geographical hotspots. Rhode Island is the smallest state by area in the United States of America. It has one million inhabitants and is comparable to many larger cities worldwide. We aimed to investigate the possible associations of pOHCA with economic factors and the coronavirus 2019 (COVID-19) pandemic. Our goal was to identify high-risk areas and evaluate whether the COVID-19 pandemic had an influence on delays in prehospital care. We analyzed all pOHCA cases (patients <18 years of age) in Rhode Island between March 1, 2018-February 28, 2022. We performed Poisson regression with pOHCA as dependent and economic risk factors (median household income [MHI] and child poverty rate from the US Census Bureau) as well as the COVID-19 pandemic as independent variables. Hotspots were identified using local indicators of spatial association (LISA) statistics. We used linear regression to assess the association of emergency nedical services-related times with economic risk factors and COVID-19. A total of 51 cases met our inclusion criteria. Lower MHIs (incidence-rate ratio [IRR]) 0.99 per $1,000 MHI; P=0.01) and higher child poverty rates (IRR 1.02 per percent; P=0.02) were significantly associated with higher numbers of ambulance calls due to pOHCA. The pandemic did not have a significant influence (IRR 1.1; P=0.7). LISA identified 12 census tracts as hotspots (P<0.01). The pandemic was not associated with delays in prehospital care. Lower median household income and higher child poverty rate are associated with higher numbers of pediatric out-of-hospital cardiac arrest.

The green quality of urban spatial development: A multi-dimensional and multi-regional model using big data

Spatial development involves multi-dimensional and multi-regional objectives that might be competing, while few studies have examined these issues simultaneously. We proposed a comprehensive model to assess the green quality of urban spatial development (GQUSD), focusing on the synergy of multi-dimensional objectives and the balance of multi-regional interests. The metropolitan city of Hangzhou was taken as an example. We included land consumption, vegetation loss and infrastructure cost as the inputs and the density of production and living activities as the outputs of spatial development. We developed a categorical Data Envelope Analysis (DEA) using geospatial big data and stratified optimization to evaluate the synergy of inputs-outputs at the finer level of spatial grids. Resultsshow a “sandwich” structure of the GQUSD in Hangzhou when considering green space and vegetation loss, and characterize four spatial typologies of agglomerated development, limited development, blinded development and low-potential development through the Bivariate Local Indicator of Spatial Association (LISA) analysis. The “sandwich” structure is shaped by spatial processes of socio-economic agglomeration, industrial-driven sprawl, new-town development and suburbanization of high-end sectors. We also find green inequality in the spatial development of Hangzhou where vegetation costs were higher in manufactural industrial sites and low-income communities. We provide urban planning strategies to balance competing interests across regions, objectives, functions and communities for green growth and sustainable goals.

Urban delivery, the Covid-19 pandemic and changes in land use: evidence of the territorial dynamics of the non-residential real estate sector in Belo Horizonte

In addition to the observed impacts on urban mobility, urban deliveries have changed the territorial dynamics of the non-residential real estate sector. This paper analyses land-use changes in the non-residential real estate sector caused by urban deliveries in 2020, in the context of the COVID-19 pandemic in Belo Horizonte. Therefore, we used a spatial-temporal approach based on Local Indicators of Spatial Association and the estimation of a geographically weighted regression model. The results showed the decentralization of new businesses occurred and that there is an inverse relationship between traditional retail deliveries and online retail deliveries concerning the location of new businesses. Furthermore, the results indicated a change in the territorial dynamics of the non-residential real estate sector, encouraged by urban deliveries. Thus, the master plans must consider urban deliveries as a driving component in urban and transport planning.

Exploring the changes and driving mechanisms in the production-transport-consumption process of ecosystem services flow in the Yellow River Basin under the background of policy changes

Analyzing the spatiotemporal changes of the ecosystem services flow and the impact mechanism is of great significance for achieving human well-being. It is challenged by regional differences in resource utilization pattern and intensity and the complex mechanisms of action between ecosystem services supply and demand. This study used InVEST model and local indicator of spatial association to delineate ecosystem services supply and demand areas. This study used comparative ecological radiation force to characterize the flow of carbon sequestration from supply to demand areas, and its changes were analyzed. The influence of different factors on the carbon sequestration supply, demand and flow was analyzed using correlation analysis. The results showed that there were significant spatial differences in the supply–demand situation of the Yellow River Basin. In this study, 7 supply areas and 29 demand areas were delineated. Many ecological restoration policies implemented from 2000 to 2020 improved the environmental situation and enhanced carbon sequestration flow. Carbon sequestration flow was more sensitive to human activities. The impact of the same intensity of human activity on the supply potential of carbon sequestration was stronger than the supply capacity. This study can provide a reference for regional ecosystem management.

Evaluation of binge drinking behavior among working-age populations in Thailand using GIS: A national survey in 2017

This study identifies the prevalence of binge drinking behaviour and the statistical relationship between local patterns and binge drinking behaviour among the working-age population in Thailand. This study was conducted using The Smoking and Drinking Behaviour Survey 2017 data set from the National Statistical Office of Thailand. Local Indicators of Spatial Association (LISA) were used to identify the spatial autocorrelation between surrounding areas, with binge drinking behaviour among the working-age population in Thailand. Findings showed that among the 61,708 participants, 11.47% engaged in binge drinking behaviour. There was aspatial global autocorrelation between the surrounding areas and binge drinking behaviour among the working-age population, with a Moran’s I value of 0.579. The LISA analysis indicated nine hotspots or high–high clusters. In Thailand, there is a cultural context that encourages risky behaviour such as drinking alcohol beyond the standards set by law. Therefore, to manage this problem efficiently and effectively, the public and private sectors and related agencies should raise awareness about the health effects of binge drinking and promote health literacy to local residents while engaging all sectors in managing alcohol behaviour in all areas. This type of approach is sustainable and can have long-term effects on society.

Quantifying the spatiotemporal dynamics and impact factors of China's county-level carbon emissions using ESTDA and spatial econometric models

China currently has the largest total CO2 emissions and is one of the countries that has made the most efforts to cut these. Anthropogenic emissions from county-level districts play a pivotal role in meeting carbon neutral due to the favorable breakdown of reduction targets into sub-national units, whereas limited work has been done related to those features and determinants. Here, we attempted to quantify the spatiotemporal dynamics of county carbon emissions and their drivers in China between 2000 and 2020 using an exploratory space-time data analysis (ESTDA) and spatial econometric method based on a remote sensing image inversion dataset. The results showed that emissions per capita and per unit of GDP in Chinese counties took on drastically opposite stances amid a trend of increasing carbon emissions. Meanwhile, their carbon emissions exhibited a pronounced regional disparity and spatially positive autocorrelation, characterized by sharp spatial heterogeneity and clustering. Local indicators of spatial association implied that the patterns of county carbon emissions had certain spatial integration, and that locally correlated forms were represented by deep path dependence and spatial locking effects. Besides, a range of panel regression models provided evidence of endogenous interactions of county carbon emissions, notably where every 1% increase in the neighboring emissions induced a local emissions increase by at least 0.4%. Various factors employed not only exerted a direct impact on local carbon emissions, but had spatial spillover effects on neighboring districts. Of these, economic level and industrial structure presented a significantly positive relationship with carbon emissions, while population clustering, financial input and technological advance had clear inhibitory effects. Our findings cast fresh light on the importance of the socioeconomic diversity of a district and its neighbors for government policy decisions related to carbon abatement at the county level.

Ethnic factor in electoral behavior in Latvia: spatial effect

The article analyses the way the spatial effect is reflected in the ethnic voting in Latvia at the parliamentary elections of 2011, 2014 and 2018. The study reveals that the electoral support of both parties relying on Russian-speaking voters correlates with the share of Russians living in a municipality. However, in case of Social Democratic Party "Harmony" the correlation is higher than that of the Latvian Russian Union. The local indicators of spatial association display neighborhood clusters of high support of these parties in Latgale and those of low support in Kurzeme. The shape of these clusters does not change considerably from one electoral cycle to another. Municipalities with primarily non-Latvian population in Eastern Latgale close to the border with Russia and Belarus differ in their electoral behavior the most. Electoral zoning of Latvia by the method of spatially constrained hierarchical clustering based on the results of all parliamentary parties first and foremost indicates the cluster close to the border of Latvia with Russia and Belarus. Moreover, the cleavage in electoral behavior between these Latgalian municipalities and other Latvian regions appears to be the strongest not only in the Latvian context, but in comparison with electoral cleavages in eleven other countries bordering Russia.

Spatio-temporal analysis and identification of deforestation hotspots in the Moroccan western Rif

In the north of Morocco, forest areas are under threat mainly due to the expansion of agricultural land, forest fires and socio-demographic, economic and urban dynamics. Despite the magnitude of deforestation in this area, quantitative studies and investigations of its evolution in time and space are rare or non-existent. The present study aims to assess and quantify gross forest losses in the Western Rif between 2001 and 2020 through the exploitation of Global Forest Change (GFC) data developed by Hansen et al. (version 1.8) based on high resolution Landsat TM satellite images. We used Local Indicators of Spatial Association (LISA) methods to identify statistically significant spatio-temporal trends in forest losses with ArcGis 10.8 and Geoda software. The main results showed that over the last two decades, the Western Rif has lost 8.39% of its forest heritage, a total area of 272 km2. The greatest forest losses were recorded in the provinces of Chefchaouen, Tetouan and Larache (70% of total losses), while the highest annual deforestation rates are reported in the prefectures of M'diq-Fnideq and Tangier-Assilah (0.9% and 0.89% respectively). Moreover, we observed that 81% of the deforested areas were recorded in the formations of the following types: matorral, cork oak stands and artificial pine forest. A first analysis of local Moran's spatial autocorrelation based on the communal division and on the variables relating to forest losses and gross deforestation rates revealed the existence of 3 significant clusters of the high-high (HH) type. A second optimized analysis of deforestation hotspot (Getis-Ord statistical test) based on a 0.5 km × 0.5 km grid identified 26 deforestation fronts in the study area. Finally, we conducted an emerging hotspot analysis to take into account the time dimension and we concluded that the oscillating deforestation model predominates in the Western Rif. These results constitute valuable decision elements to better guide, prioritize and rationalize the monitoring, conservation and restoration efforts of forest ecosystems.

Exploring the association of Brownfield remediation status with socioeconomic conditions in Wayne County, MI

Urban neighborhoods with locations of environmental contamination, known as brownfields, impact entire neighborhoods, but corrective environmental remedial action on brownfields is often tracked on an individual property basis, neglecting the larger neighborhood-level impact. This study addresses this impact by examining spatial differences between brownfields with unmitigated environmental concerns (open site) and sites that are considered fully mitigated or closed in urban neighborhoods (closed site) on the US census tract scale in Wayne County, MI. Michigan’s Department of Environment, Great Lakes, and Energy’s leaking underground storage tank (LUST) database provided brownfield information for Wayne County. Local indicators of spatial association (LISA) produced maps of spatial clustering and outliers. A McNemar’s test demonstrated significant discordances in LISA categories between LUST open and closed sites (p < 0.001). Geographically weighted regressions (GWR) evaluated the association between open and closed site spatial density (open-closed) with socioeconomic variables (population density, proportion of White or Black residents, proportion of college educated populations, the percentage of owner-occupied units, vacant units, rented units, and median household value). Final multivariate GWR showed that population density, being Black, college education, vacant units, and renter occupied units were significantly associated (p < 0.05) with open-closed, and that those associations varied across Wayne County. Increases in Black population was associated with increased open-closed. Increases in vacant units, renter-occupied units, and college education were associated with decreased open-closed. These results provide input for environmental justice research to identify inequalities and discover the distribution of environmental hazards among urban neighborhoods.

Urban Green Space Analysis and its Effect on the Surface Urban Heat Island Phenomenon in Denpasar City, Bali

The Urbanization process in Indonesia’s big cities causes adverse environmental impacts such as climate change and land cover change. Urban climate change causes the warming of urban areas compared to rural areas; it is called Urban Heat Island phenomenon. Loss of vegetation due to urban development is one of several causes that contribute to urban heat islands. This study examines the availability of green spaces and their effects on the surface urban heat island in Denpasar city. This study used the spatial approach for Urban Green space mapping with digitizing methods. Landsat 8's thermal band is used for land surface temperature mapping and to conduct a spatial pattern analysis of the SUHI phenomena. The Global Moran’s Index and Local Indicator of Spatial Association (LISA) were used to determine the correlation between urban green space and SUHI. The study result shows that Denpasar City's urban green space area covers 28.22 km2. That's equal to 22.1% of the Denpasar City Administrative area. Denpasar Selatan district has the largest urban green space cover, with 14.19 km2 covered, or 50.27% of all the green space in Denpasar City. The majority of Denpasar is affected by UHI occurrences, except the northern region of North Denpasar and the southern region of South Denpasar. The maximum UHI level reaches 4-5°C, located on the east side of South Denpasar, especially in the Sanur coastal area. According to the spatial pattern study, the association between urban green space and SUHI only exists on the north side of Denpasar. The correlation between low-SUHI intensity clusters and high cover of green space is shown in the same area. However, the association between High-UHI intensity and low green space cover has not significantly happened. It indicated that other factors besides green space could affect the land surface temperature.

Spatial Analysis of Sustainability Measures from Agritourism in Iberian Cross-Border Regions

This research aims to evaluate whether agritourism is a sustainable activity, comparing the profiles of accommodation, farmers, and accommodation with agricultural activities. Four specific objectives drive this study: (a) examining the cultural/landscape, economic, environmental, and social indicators of sustainability; (b) comparing indicators of sustainability between groups; (c) revealing whether there is a significant cross-correlation between spatial distribution and its impact on sustainability metrics; (d) discussing the significance of these factors for tourism development policies. The present study uses various techniques to study the degree of equilibrium in the distribution of the accommodation and farms in relation to their sustainable impact. To verify this, we use the global Moran’s I and G (d) tests proposed by Getis and Ord. As well as analyzing local contrasts, LISA (Local Indicators of Spatial Association) and Hot Spot analysis are used for mapping. The main results reveal different profiles of sustainability, highlighting the accommodation profile with the lowest contribution to sustainable development, while profiles where the relationship between agriculture and tourism is most visible seem to perform better. In general, the main results obtained suggest that there is no direct relationship between spatial distribution and sustainability inputs, excluding the potential of endogenous resources, and suggesting the existence of imbalances in the creation of agritourism products.

Distribution of Coronavirus Disease (Covid-19) in West Sumatra Province with Local Indicator of Spatial Association (LISA) Cluster Map

&lt;div&gt;&lt;table cellspacing="0" cellpadding="0" align="left"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td align="left" valign="top"&gt;&lt;p&gt;&lt;em&gt;The COVID-19 pandemic in West Sumatra Province indicates a greater number of cases and mortality. The spread of COVID-19 is related to the mobility of the population, so the potential for transmission between regions is difficult to control. This study aims to determine the local index used in evaluating the tendency for local spatial groupings and can show some form of spatial relationship. Spatial analysis were conducted on 2020 to analyze spatial distribution of Covid-19 in West Sumatera Province. Spatial relationship was assessed by Local Indicator of Spatial Association (LISA). Mapping can be done with the LISA cluster map. The data used is COVID-19 incidence data based on reports from district or city in West Sumatra Province in 4 June 2020. We used Open Geoda Software to analyze the spatial distribution. There is positive spatial autocorrelation and classification in hot spots, cold spots, and outliers in the spread of COVID-19 cases in West Sumatra Province. Hot spots were found in Padang City, Bukittinggi City, and Padang Panjang City. Cold spots also detected in several districts, that is &lt;/em&gt;&lt;em&gt;West Pasaman, Pasaman, Payakumbuh, Solok, Padang Pariaman, Pariaman, Sawahlunto, and Sijunjung. &lt;/em&gt;&lt;em&gt;The transmission of the COVID-19 case does not recognize regional boundaries, but the grouping of districts or cities based on regional vulnerability is important as part of local control efforts to allocate resources. Coordination and collaboration among local governments need to be strengthened in preventing transmission between regions and reducing the number of cases in vulnerable areas based on hot spots and cold spots from LISA cluster map. It’s necessary for intervention programs more focused and effectively.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords: COVID-19, Mapping, Spatial, Vulnerability&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;