Indicators Of Spatial Autocorrelation Research Articles

Geospatial clustering of childhood IgA vasculitis and IgA vasculitis-associated nephritis

ObjectivesResearch on spatial variability of the incidence of IgA vasculitis (IgAV) in children and its potential implications for elucidation of the multifactorial aetiology and pathogenesis is limited. We intended to...

Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level.

The Islamic Republic of Iran reported its first COVID-19 cases by 19th February 2020, since then it has become one of the most affected countries, with more than 73,000 cases and 4,585 deaths to this date. Spatial modeling could be used to approach an understanding of structural and sociodemographic factors that have impacted COVID-19 spread at a province-level in Iran. Therefore, in the present paper, we developed a spatial statistical approach to describe how COVID-19 cases are spatially distributed and to identify significant spatial clusters of cases and how socioeconomic and climatic features of Iranian provinces might predict the number of cases. The analyses are applied to cumulative cases of the disease from February 19th to March 18th. They correspond to obtaining maps associated with quartiles for rates of COVID-19 cases smoothed through a Bayesian technique and relative risks, the calculation of global (Moran's I) and local indicators of spatial autocorrelation (LISA), both univariate and bivariate, to derive significant clustering, and the fit of a multivariate spatial lag model considering a set of variables potentially affecting the presence of the disease. We identified a cluster of provinces with significantly higher rates of COVID-19 cases around Tehran (p-value< 0.05), indicating that the COVID-19 spread within Iran was spatially correlated. Urbanized, highly connected provinces with older population structures and higher average temperatures were the most susceptible to present a higher number of COVID-19 cases (p-value < 0.05). Interestingly, literacy is a factor that is associated with a decrease in the number of cases (p-value < 0.05), which might be directly related to health literacy and compliance with public health measures. These features indicate that social distancing, protecting older adults, and vulnerable populations, as well as promoting health literacy, might be useful to reduce SARS-CoV-2 spread in Iran. One limitation of our analysis is that the most updated information we found concerning socioeconomic and climatic features is not for 2020, or even for a same year, so that the obtained associations should be interpreted with caution. Our approach could be applied to model COVID-19 outbreaks in other countries with similar characteristics or in case of an upturn in COVID-19 within Iran.

Air pollutants and risk of death due to COVID-19 in Italy

The present work aims to study the role of air pollutants in relation to the number of deaths per each Italian province affected by COVID-19. To do that, specific mortality from COVID-19 has been standardized for each Italian province and per age group (10 groups) ranging from 0 to 9 years to >90 years, based on the 2019 national population figures. The link between air pollutants and COVID-19 mortality among Italian provinces was studied implementing a linear regression model, whereas the wide set of variables were examined by means of LISA (Local Indicators of Spatial Autocorrelation), relating the spatial component of COVID-19 related data with a mix of environmental variables as explanatory variables. As results, in some provinces, namely the Western Po Valley provinces, the SMR (Standardized Mortality Ratio) is much higher than expected, and the presence of PM10 was independently associated with the case status. Furthermore, the results for LISA on SMR and PM10 demonstrate clusters of high-high values in the wide Metropolitan area of Milan and the Po Valley area respectively, with a certain level of overlap of the two distributions in the area strictly considered Milan. In conclusion, this research appears to find elements to confirm the existence of a link between pollution and the risk of death due to the disease, in particular, considering land take and air pollution, this latter referred to particulate (PM10). For this reason, we can reiterate the need to act in favour of policies aimed at reducing pollutants in the atmosphere, by means of speeding up the already existing plans and policies, targeting all sources of atmospheric pollution: industries, home heating and traffic.

Exploring the equality of accessing urban green spaces: A comparative study of 341 Chinese cities

Despite multiple benefits of green space for urban residents, the investigations on equality of access to green space are scarce. Most focused on individual cities and the results were inconsistent due to the varying definitions, scales, and evaluation methods employed within each. This study intends to address this gap by developing two indexes based on the Gini Coefficient to compare the inequality of access to urban green spaces across all 341 prefecture-level cities of China. An urban green space equality index (UGSE) measures the distribution of overall urban vegetation within a city. A park green space equality index (PGSE) focuses on the equality level of public access to parks.The UGSE and PGSE at the national scale are 0.490 and 0.848. Strong disparities have been found in both UGSE and PGSE across the country using the local indicator of spatial autocorrelation (LISA). Regression analysis shows equality of green space is significantly related to GDP per capita, population density, and urban land area. Urban greening related policies are not significantly associated with equality, suggesting that a lack of concern might exist among Chinese cities for the spatial equality of urban green space implementation. These findings can help decision makers evaluate regional disparities in urban green space equality, thereby providing location-specific interventions for more inclusive and equal provisioning of urban green spaces.

Spatio-Temporal Variations of CO2 Emission from Energy Consumption in the Yangtze River Delta Region of China and Its Relationship with Nighttime Land Surface Temperature

The rapid development of industrialization and urbanization has resulted in a large amount of carbon dioxide (CO2) emissions, which are closely related to the long-term stability of urban surface temperature and the sustainable development of cities in the future. However, there is still a lack of research on the temporal and spatial changes of CO2 emissions in long-term series and their relationship with land surface temperature. In this study, Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) data, Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) composite data, energy consumption statistics data and nighttime land surface temperature are selected to realize the spatial informatization of long-term series CO2 emissions in the Yangtze River Delta region, which reveals the spatial and temporal dynamic characteristics of CO2 emissions, spatial autocorrelation distribution patterns and their impacts on nighttime land surface temperature. According to the results, CO2 emissions in the Yangtze River Delta region show an obvious upward trend from 2000 to 2017, with an average annual growth rate of 6.26%, but the growth rate is gradually slowing down. In terms of spatial distribution, the CO2 emissions in that region have significant regional differences. Shanghai, Suzhou and their neighboring cities are the main distribution areas with high CO2 emissions and obvious patch distribution patterns. From the perspective of spatial trend, the areas whose CO2 emissions are of significant growth, relatively significant growth and extremely significant growth account for 8.78%, 4.84% and 0.58%, respectively, with a spatial pattern of increase in the east and no big change in the west. From the perspective of spatial autocorrelation, the global spatial autocorrelation index of CO2 emissions in the Yangtze River Delta region in the past 18 years has been greater than 0.66 (p &lt; 0.01), which displays significant positive spatial autocorrelation characteristics, and the spatial agglomeration degree of CO2 emissions continues to increase from 2000 to 2010. From 2000 to 2017, the nighttime land surface temperature in that region showed a warming trend, and the areas where CO2 emissions are positively correlated with nighttime land surface temperature account for 88.98%. The increased CO2 emissions lead to, to a large extent, the rise of nighttime land surface temperature. The research results have important theoretical and practical significance for the Yangtze River Delta region to formulate a regional emission reduction strategy.

Spatial epidemiology of cystic echinococcosis in livestock from a hyper-endemic region in southern Chile

Cystic echinococcosis (CE) is a parasitic zoonosis, caused by the larval stage of Echinococcus granulosus. This disease has a worldwide distribution and is considered an important zoonosis in Chile, especially in regions where raising livestock dominates. We aimed to describe the spatial distribution and risk factors for CE in cattle and sheep slaughtered in the Aysén Region, Chile between 2015 and 2016, to inform disease control in this hyper-endemic region. Clustering and hotspot analyses of CE at the farm-level were performed (Moran’s global index, local indicator of spatial autocorrelation (LISA), and the scan statistic Poisson model), and we used spatial interpolation to display areas with a higher risk of CE. Mixed effects logistic regression models were fit to assess the association between municipality of origin and age of animals and CE detected at slaughter, with the farm of origin included as a random effect.Overall, data from 1532 cattle farms (1078 geocoded) and 30,805 cattle were analyzed, with a 64 % and 39 % CE prevalence at the farm and animal-level, respectively. For sheep farms, data from 381 (296 geocoded) farms and 58,223 sheep were analyzed, with a 71 % and 18 % CE prevalence at the farm and animal-level, respectively. Globally, the CE prevalence in both cattle and sheep farms was not clustered. However, we found 14 cattle and 23 sheep farms with high prevalence that were locally clustered. We also detected 6 and 11 clusters in cattle and sheep farms, respectively. Risk of reported CE varied according to municipalities of origin, and the age of animals.This study confirms that the Aysén region is a hyper-endemic region for CE. It demonstrates the usefulness of the traceability of animals slaughtered for understanding the epidemiology of CE. The results suggest that in this hyper-endemic region in which there are limited financial resources available, that intervention activities ― at the beginning of a control program ― should be focused on farms with high prevalence and identified disease clusters.

Spatial-Temporal Analysis of Point Distribution Pattern of Schools Using Spatial Autocorrelation Indices in Bojnourd City

In recent years, attention has been given to the construction and development of new educational centers, but their spatial distribution across the cities has received less attention. In this study, the Average Nearest Neighbor (ANN) and the optimized hot spot analysis methods have been used to determine the general spatial distribution of the schools. Also, in order to investigate the spatial distribution of the schools based on the substructure variables, which include the school building area, the results of the general and local Moran and Getis Ord analyses have been investigated. A differential Moran index was also used to study the spatial-temporal variations of the schools’ distribution patterns based on the net per capita variable, which is the amount of school building area per student. The results of the Average Nearest Neighbor (ANN) analysis indicated that the general spatial patterns of the primary schools, the first high schools, and the secondary high schools in the years 2011, 2016, 2018, and 2021 are clustered. Applying the optimized hot spot analysis method also identified the southern areas and the suburbs as cold polygons with less-density. Also, the results of the differential Moran analysis showed the positive trend of the net per capita changes for the primary schools and first high schools. However, the result is different for the secondary high schools.

Pedestrian Fatal Crash Location Analysis in Ohio using Exploratory Spatial Data Analysis Techniques

Pedestrian safety is a top priority within the transportation planning community as cities promote sustainable transportation, alternative travel modes, and healthy lifestyles. If people feel unsafe while walking, they will choose other modes of transportation if they are able. To prioritize safety, it is important to know where pedestrian crashes are occurring and with what severity. Using spatial statistical methods including nearest neighbor index, Moran’s I, local indicators of spatial autocorrelation (LISA), and Getis-Ord G-statistic, this study seeks to analyze the pedestrian fatality locations within the state of Ohio over a 10-year period (2007–2016) to identify hot spots, cold spots, and spatial patterns across three different spatial scales: county, census tract, and traffic analysis zone (TAZ). It seeks to understand the effects of aggregated data across these spatial scales on the outcome of the analysis and determine the most useful spatial scale at which to study pedestrian fatalities. The study concludes that spatial analyses at small scales are most informative. It goes on to recommend locations within Ohio for further analysis based on the resulting maps, including areas with outliers. As of writing this, there is no current statewide pedestrian fatality analysis for the state of Ohio.

Tree Line Identification and Dynamics under Climate Change in Wuyishan National Park Based on Landsat Images

The alpine tree line ecotone, reflecting interactions between climate and ecology, is very sensitive to climate change. To identify tree line responses to climate change, including intensity and local variations in tree line advancement, the use of Landsat images with long-term data series and fine spatial resolution is an option. However, it is a challenge to extract tree line data from Landsat images due to classification issues with outliers and temporal inconsistency. More importantly, direct classification results in sharp boundaries between forest and non-forest pixels/segments instead of representing the tree line ecotone (three ecological regions—tree species line, tree line, and timber line—are closely related to the tree line ecotone and are all significant for ecological processes). Therefore, it is important to develop a method that is able to accurately extract the tree line from Landsat images with a high temporal consistency and to identify the appropriate ecological boundary. In this study, a new methodology was developed based on the concept of a local indicator of spatial autocorrelation (LISA) to extract the tree line automatically from Landsat images. Tree line responses to climate change from 1987 to 2018 in Wuyishan National Park, China, were evaluated, and topographic effects on local variations in tree line advancement were explored. The findings supported the methodology based on the LISA concept as a valuable classifier for assessing the local spatial clusters of alpine meadows from images acquired in nongrowing seasons. The results showed that the automatically extracted line from Landsat images was the timber line due to the restriction in spatial autocorrelation. The results also indicate that parts of the tree line in the study area shifted upward vertically by 50 m under a 1 °C temperature increase during the period from 1987 to 2018, with local variations influenced by slope, elevation, and interactions with aspect. Our study contributes a novel result regarding the response of the alpine tree line to global warming in a subtropical region. Our method for automatic tree line extraction can provide fundamental information for ecosystem managers.

Spatial correlation of cultural industry and tourism industry in Shaanxi Province, China: LISA analysis based on coordination model

ABSTRACT Shaanxi is not only representative of Chinese traditional culture but also the province with the most abundant tourism resources. In this study, the LISA (Local Indicators of Spatial Auto-correlation) tests were used to investigate the development of the cultural industry and the tourism industry in Shaanxi. The results indicated that from 2010 to 2018, the coordinated development, of cultural and tourism industries, has gradually formed a positive aggregation in space. The rapid development of tourism and cultural industries in Xi’an has driven the development of the surrounding areas (Xianyang, Baoji, and Weinan) and formed a high-value cluster in Guanzhong.

N-(3-oxododecanoyl)-homoserine lactone regulates osteoblast apoptosis and differentiation by mediating intracellular calcium

Pseudomonas aeruginosa (P. aeruginosa) is associated with periapical periodontitis. The lesions are characterized by a disorder in osteoblast metabolism. Quorum sensing molecular N-(3-oxododecanoyl)-homoserine lactone (AHL) is secreted by P. aeruginosa and governs the expression of numerous virulence factors. AHL can trigger intracellular calcium ([Ca2+]i) fluctuations in many host cells. However, it is unclear whether AHL can regulate osteoblast metabolism by affecting [Ca2+]i changes or its spatial correlation. We explored AHL-induced apoptosis and differentiation in pre-osteoblastic MC3T3-E1 cells and evaluated [Ca2+]i mobilization using several extraction methods. The spatial distribution pattern of [Ca2+]i among cells was investigated by Moran's I, an index of spatial autocorrelation. We found that 30 μM and 50 μM AHL triggered opposing osteoblast fates. At 50 μM, AHL inhibited osteoblast differentiation by promoting mitochondrial-dependent apoptosis and negatively regulating osteogenic marker genes, including Runx2, Osterix, bone sialoprotein (Bsp), and osteocalcin (OCN). In contrast, prolonged treatment with 30 μM AHL promoted osteoblast differentiation concomitantly with cell apoptosis. The elevation of [Ca2+]i levels in osteoblasts treated with 50 μM AHL was spatially autocorrelated, while no such phenomenon was observed in 30 μM AHL-treated osteoblasts. The blocking of cell-to-cell spatial autocorrelation in the osteoblasts provoked by 50 μM AHL significantly inhibited apoptosis and partially restored differentiation. Our observations suggest that AHL affects the fate of osteoblasts (apoptosis and differentiation) by affecting the spatial correlation of [Ca2+]i changes. Thus, AHL acts as a double-edged sword for osteoblast function.

A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany

BackgroundAs of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale.MethodsBy sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations.ResultsA strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables’ partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure.ConclusionsThe multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.

Mortality by cause of death in Colombia: a local analysis using spatial econometrics

Colombia is undergoing major changes in mortality patterns. National- and department-level cause-specific analyses have previously been carried out, but very little is known about municipal-level trends, despite their epidemiological interest. We first analyze standardized mortality rates for seven cause-of-death groups to obtain high and low mortality clusters based on the spatial autocorrelation indicators Global Moran’s I and Local Moran’s I. The Mann–Whitney nonparametric test is then used to ascertain statistical associations between the high and low mortality clusters and known health determinants. We subsequently apply spatial lag and Durbin (when spatial autocorrelation was present) and OLS models (when not) to explain overall spatial patterns in cause-specific mortality. Age- and sex-specific cause-of-death mortality and population data were obtained from the National Administrative Department of Statistics (DANE). Deaths were corrected for each municipality due to under-registration. Results show that spatial autocorrelation declined over time for all cause-of-death categories, except male circulatory system diseases and perinatal mortality. It is highest in external causes, especially among men, with mortality hotspots moving from the central Andean area to Orinoquia and the Amazon rainforest. Male mortality is also more spatially clustered than female mortality and especially neoplasms, and external-cause mortality is also indirectly affected by the conditions of neighboring municipalities. Municipal surface area, ethnicity and public expenditure on health and education are the most frequent contextual variables explaining territorial differences in mortality. The identification of geographical mortality clusters in Colombia will allow decision makers to prioritize those regions with higher mortality.

Gender Gaps in the Use of Urban Space in Seoul: Analyzing Spatial Patterns of Temporary Populations Using Mobile Phone Data

This study aims to examine the gender gaps in the use of urban space in Seoul, Korea, to provide empirical evidence for urban planning for gender equality. We analyzed daily temporary populations that were estimated using mobile phone data. We used the total, women’s, and men’s temporary populations as well as the subtraction of the temporary population of men from that of women (SMW) as dependent variables. We first conducted a visual analysis on temporary population density using kernel density estimation and then conducted a further analysis using spatial autocorrelation indicators and spatial regression models. The results demonstrate that: (1) Temporary population patterns for women and men showed similarities in that both were larger in business areas than in residential areas, which means that a large number of women were engaged in economic activities like men; (2) the pattern for SMW showed the opposite, that is, women were more active in residential areas and areas where neighborhood retail shops, cultural facilities, parks, and department stores were easily accessible; and (3) both women’s temporary population and SMW had spatial autocorrelation and thus showed clustering patterns that can be helpful in urban planning for gender equality in Korea.

New framework of Getis-Ord's indexes associating spatial autocorrelation with interaction.

Spatial autocorrelation and spatial interaction are two important analytical processes for geographical analyses. However, the internal relations between the two types of models have not been brought to light. This paper is devoted to integrating spatial autocorrelation analysis and spatial interaction analysis into a logic framework by means of Getis-Ord’s indexes. Based on mathematical derivation and transform, the spatial autocorrelation measurements of Getis-Ord’s indexes are reconstructed in a new and simple form. A finding is that the local Getis-Ord’s indexes of spatial autocorrelation are equivalent to the rescaled potential energy indexes of spatial interaction theory based on power-law distance decay. The normalized scatterplot is introduced into the spatial analysis based on Getis-Ord’s indexes, and the potential energy indexes are proposed as a complementary measurement. The global Getis-Ord’s index proved to be the weighted sum of the potential energy indexes and the direct sum of total potential energy. The empirical analysis of the system of Chinese cities are taken as an example to illustrate the effect of the improved methods and measurements. The mathematical framework newly derived from Getis-Ord’s work is helpful for further developing the methodology of geographical spatial modeling and quantitative analysis.

Analysis and spatial distribution of schistosomiasis mansoni in a historically endemic area of northeastern Brazil.

To perform a descriptive analysis of the activities of the Schistosomiasis Control Program, as well as the spatial distribution of the condition in the state of Alagoas, Brazil, for the period from 2007 to 2016. Descriptive ecological study. Data from positive human cases and operational data were collected in the Information System of the Schistosomiasis Control Program, and data for spatial analysis were collected on the website of the Brazilian Institute of Geography and Statistics. An analysis of spatial autocorrelation (Moran statistics) was performed, where a spatial pattern was established, which showed the Q1 and Q2 patterns to be the most important, and Q3 and Q4 representing transition areas. In the years under study, at least 85% (n=60/70) of the municipalities carried out the activities recommended by the PCE (Schistosomiasis Control Program). Alagoas presented an average positivity rate of 7.1%, which is very high compared to the prevalence of 3.3% at the last national schistosomiasis survey conducted between 2010 and 2015. Moran's statistics showed 22/70 municipalities forming a Q1 cluster, of high/high pattern, and 32/70 municipalities forming a Q2 cluster, of low/low pattern, with the others in a transition area. Moran Map data, however, showed only 7/70 municipalities in the endemic area with a spatial autocorrelation, with these municipalities having the Mundau River as a common element. Schistosomiasis mansoni is of great importance for public health in Alagoas and that the use of spatial analysis can identify priority areas for preventive and control measures against schistosomiasis mansoni.

Analisis Spasial Persebaran Dan Pemetaan Kerawanan Daerah Titik Panas Di Kalimantan Timur Menggunakan Spatial Pattern Analysis Dan Flexibly Shaped Spatial Scan Statistic

The statistical method used to analyze spatial data and spatial statistics. The case study in this study is the average number of hotspots in East Kalimantan by Regency / City in 2016-2018. This study aims to analyze the presence or absence of spatial autocorrelation in the data on the number of hotspots, determine the distribution pattern of hotspots, as well as determine the level of vulnerability of potential areas for forest and land fires in East Kalimantan by Regency / City in 2016-2018. The method used to analyze spatial autocorrelation globally and determine the distribution patterns is the Moran Index. Local Indicator of Spatial Autocorrelation (LISA) to analyze spatial autocorrelation locally. Spatial autocorrelation analysis results globally using the Moran Index with α = 25% shows that there is a spatial autocorrelation in the average number of hotspots in East Kalimantan in 2016-2018. The distribution pattern of hotspots in East Kalimantan is the pattern of spread which shows that in 2016-2018 the hotspots spread in each district / city of East Kalimantan. Meanwhile, the results of the local analysis using LISA showed that there were spatial autocorrelations in several districts / cities in East Kalimantan. The method used to determine the level of vulnerability of potential forest and land fires is Flexibly Shaped Spatial Scan Statistics and LISA. The results showed that the regencies / cities included in the category of forest and land fire hazard were Samarinda City, Bontang City, Kutai Kartanegara District and East Kutai.

The impact of urbanization on urban agriculture: Evidence from China

This study aims to explore the relationship between urbanization and urban agriculture in China. We first analyze the spatial correlation of urbanization and urban agriculture using the spatial Moran index. We, then, perform an empirical analysis using the Spatial Durbin Model. We find that: (1) Both urban agriculture and urbanization have spatial autocorrelation. From 2001 to 2012, the spatial autocorrelation index of urban agriculture was between 0.03 and 0.2, while urbanization reached 1.77–2.08. (2) Urbanization has generally improved per capita urban agriculture in local cities. When urbanization increases by 1%, the per capita value added of urban agriculture increases by 0.58%. (3) Urban agriculture and convenient transportation in surrounding cities will also bring spatial spillover effects to local urban agriculture. Every 1% increase in urban agriculture of surrounding areas will reduce the added value of local urban agriculture by 0.31%. However, if the transportation convenience in the surrounding area increases by 1%, the local urban agriculture will increase by 0.24%. The conclusions as below: Urbanization will affect the development of local urban agriculture. In the process of promoting urbanization, we should pay more attention to the integration of agriculture with other advantageous industries and take advantage of urbanization. The economic and social development of surrounding cities also plays a role in local urban agriculture. Local governments should strengthen the scientific and economic exchanges with surrounding cities to promote the development of local urban agriculture.

Geographic Variation in Access to Pathology and Laboratory Services in Tanzania: A Cross-Sectional Geospatial Analysis

PURPOSE Increasing noncommunicable disease burden in sub-Saharan Africa requires the urgent scale-up of pathology and laboratory medicine (PALM) services. To identify service gaps at the district level, we studied geographic variation in the correlation between travel time to health facilities and population density. METHODS We linked geospatial data for Tanzania from multiple sources. Facility locations were extracted from a comprehensive facility list in Africa. Data on geographic factors, demographics, and roads were collected from government and nonprofit databases. We classified facilities assuming increasing PALM service readiness by level: dispensaries, health centers, district hospitals, and regional/referral hospitals. We input these data into the AccessMod 5 algorithm to estimate travel time across Tanzania with 1-km resolution for each PALM classification. We then calculated district-level averages of population and travel time for each PALM category. Associations between these variables were estimated using a bivariable local indicator of spatial autocorrelation, specifying immediate contiguity neighborhood definition. Spatial analysis was restricted to 172 contiguous districts (islands not included). Significance tests were two sided, with an α of .05. RESULTS Analysis included 5,342 dispensaries, 667 health centers, 185 district hospitals, and 34 regional/referral hospitals. Maps revealed clusters of estimated travel time in excess of 6 hours in less populated western and southern districts. More districts reported an average travel time of less than 1 hour to the nearest dispensary (69%) than to regional/referral hospitals (16%). Bivariable local indicators of spatial autocorrelation revealed few significant clusters of spatial correlations; however, significant correlations between low population density and longer travel times in neighboring districts were obtained for 13%, 16%, 15%, and 13% of districts for dispensaries, health centers, district hospitals, and regional/referral hospitals, respectively. CONCLUSION Limited variability of district-level spatial correlations suggests somewhat equitable geographic allocation of PALM services in Tanzania, with small areas of low population density and long travel times that demand additional intervention. Limitations include a lack of ascertainment of specific PALM services.

Temporal And Spatial Analysis Of Flooding And Landslide Disasters In Turkey For Period 1960-2018

Turkey, a country affected by natural disasters is natural disasters such as flooding and landslides are seen frequently. Floods and landslides are one of the common natural disasters in Turkey. The use of GIS-based spatial statistical analysis in disaster studies has increased in recent years. From this point, it is essential to examine the spatial distribution of disasters to prevent and mitigate disasters. In this study, flood and landslides disasters that occurred between 1960 and 2018 in Turkey is aimed to determine the spatial analysis using spatial autocorrelation methods. The problem of how these two disaster types distribute is the most pressing thing in disaster risk assessment. Data set was taken from the Disaster Knowledge Base system (TABB) of the Republic Of Turkey Ministry Of Interior Disaster And Emergency Management Presidency. Various indices related to spatial autocorrelation were classified and also they were explained mathematically. Spatial autocorrelation indices such as Moran's I, Getis Ord G, and Geary C were used to determine whether there were clustering in the data set. The rates of flood and landslide in the provinces showing clustering and their distribution are examined and also Flood and landslide distributions in clustered provinces were examined, tested with indices and compared and the results are presented in detail.