Local Moran Index Research Articles

Environmental influence on the hantavirosis incidence in the federal district, Brazil / Influência ambiental na incidência da hantavirose no distrito federal, Brasil

Most of studies dealing with hantaviruses have focused on people affected by this disease. Few studies have dedicated in the role of the land use and land cover (LULC) type in the hantavirus occurrences. The objective of this study is to analyze the influence of environmental factors in the incidence of hantavirus in the Federal District of Brazil. Probable sites of infection were identified based on the global Moran index (Moran I), local Moran index, and Getis-Ord Gi* spatial statistics analysis. Results showed that the Paranoá, Planaltina, and São Sebastião administrative regions are the critical regions in terms of occurrence of hantaviruses in the study area. altitude, categories of transition, LULC type, and geomorphology were the most important parameters related to the occurrence of the hantavirus disease. This study provides information on the effects of landscape and climatic factors on the incidence of hantavirus. The information can be used to better understand how hantavirus cardiopulmonary syndrome (HCPS) behaves in the Federal District, providing assistance for public health monitoring strategies and epidemiological surveillance.

Three-dimensional spatial modelling of traffic-induced urban air pollution using the Graz Lagrangian model and GIS

Air pollution is a significant global problem that affects climate, human, and ecosystem health. Traffic emissions are a major source of atmospheric pollution in large cities. The aim of this research was to support air quality analysis by spatially modelling traffic-induced air pollution dispersion in urban areas at the street level. The dispersion model called the Graz Lagrangian model (GRAL model) was adapted to determine the NOx concentration level based on traffic, meteorology, buildings, and street configuration data in one of Tehran’s high traffic routes. In this case, meteorological parameters such as wind speed and direction were considered significant factors. Later, using local and general auto-correlation analyses, temporal and spatial variations in the concentration of NOx were measured at different altitudes. The results showed that the average output concentration of NOx pollutants at different altitudes ranges from 64.5 to 426.6 ppb. The resulting Moran index equals to 0.7–0.9 which indicates a high level of positive spatial auto-correlation. The analysis of the local Moran index represents the overcame pollution clusters with high levels of concentration at low to medium heights and the rise in clusters with low pollution at higher heights, while there is no clear clustering in the middle sections. In addition, the study of pollutant concentration variations over time has shown that pollution peaks occur at 07:00–08:00 and 21:00–22:00.

Spatial Distribution of Mortality for Heart Failure in Brazil, 1996 - 2017.

ResumoFundamento Insuficiência cardíaca (IC) é uma das principais causas de mortalidade e morbidade no mundo, e está associada ao alto uso de recursos e custos com saúde. No Brasil, a prevalência de IC é de aproximadamente 2 milhões de pacientes, e sua incidência é de aproximadamente 240.000 novos casos por ano.Objetivo A investigação objetivou analisar a tendência espaço-temporal da mortalidade causada por IC no Brasil, de 1996 a 2017.Métodos Este é um estudo ecológico desenvolvido com dados secundários sobre mortalidade por IC no Brasil. Durante o período, 1.242.014 casos de morte causada por IC foram analisados. A existência da autocorrelação espacial de casos foi calculada utilizando o Índice de Moran Global (IMG) e, quando significativo, o Índice de Moran Local, considerando p <0,05. O risco relativo dos grupos foi calculado.Resultados A taxa de mortalidade causada por IC foi diversificada em regiões brasileiras, com ênfase no sul, sudeste e nordeste. O IMG indicou autocorrelação espacial positiva (p=0,01) em todos os períodos. Cidades localizadas no sul, sudeste, nordeste e centro-oeste mostraram maior risco relativo para mortalidade causada por IC, e a maioria das cidades do norte foi classificada como um fator protetivo contra esta causa de morte.Conclusões O estudo demonstrou declínio nas taxas de mortalidade no território nacional. A maior concentração de taxas de mortalidade está nas regiões norte e nordeste, enfatizando as áreas prioritárias de vulnerabilidade no planejamento e estratégias de controle de serviços de saúde.

Temporal and spatial trends in human visceral leishmaniasis in an endemic area in Northeast Brazil and their association with social vulnerability.

Visceral leishmaniasis is a neglected tropical disease of great importance to public health due to its wide distribution and close relationship with social and economic conditions. This study aimed to analyse the spatiotemporal dynamics of human visceral leishmaniasis (HVL) in an endemic state in the Northeast Region of Brazil and its spatial correlation with the Social Vulnerability Index (SVI) and the Municipal Human Development Index (MHDI). The study included all confirmed cases of HVL in Bahia from 2010 to 2017. A joinpoint regression model was used for trend analysis. Incidence rates were smoothed by a local empirical Bayesian model. Global and local Moran indices and space-time scan statistics were used for identification of spatial clusters. Bivariate and multivariate analyses were carried out to investigate the relationship between HVL incidence and the SVI and MHDI. Cases of HVL demonstrated stationary behaviour during the period analysed. A significant association was observed between the HVL incidence rate and social vulnerability, with high-risk clusters concentrated in the central region of the state. HVL has a strong correlation with social vulnerability in the state of Bahia. This study may provide assistance in planning actions and organizing health services to combat HVL.

‎Spatial analysis of the 10 most prevalent cancers in north-eastern Iran, 2017–2018‎

ABSTRACT This study explored spatial patterns of the 10 most prevalent cancers in north-eastern Iran. Getis-Ord G* and Anselin Local Moran's Index ‎were used for identifying ‎hotspots and local clusters. Except for breast cancer, all cancer types were more frequent among males. Cluster analysis in men showed hotspots of breast cancer in the South-West, skin cancer in the East/South-East, and brain tumours in the North. Among women, there were hotspots for skin cancer in the East and South-East, bladder cancer in the Central and East. ‎This study indicates a possible relationship between the high ‎incidence of certain cancers and environmental factors.

Characterizing spatio-temporal patterns of multi-scalar drought risk in mainland China

Drought is complex geographical elements, which endangers China's food security, with coexistence of spatial and temporal heterogeneity and similarity. Thus, the recent abnormal characteristics of Chinese droughts warrant further study. This paper proposes a three-step framework to identify the spatiotemporal evolution characteristics of drought events in China Mainland, using the standardized precipitation evapotranspiration index (SPEI) at different time scales (1, 6 and 12 months). And then the drought characteristics are separated using the run theory. Finally, the spatiotemporal evolution patterns of meteorological, agricultural and hydrological drought risk in distance are investigated based on the geographic statistics analysis method (i.e., global and local Moran's index). Results show that the drought trend is gradually increasing in some basins, especially the Yellow River Basin, the Hai River Basin and the Southwest River Basins. The multi-variate attributes of drought characteristics in spatiotemporal evolution patterns at different time scales have the characteristics of regularity and mutation, for example, the drought frequency in northwestern China shows a low values in a low value neighbourhood, but the drought duration and intensity shows a high values in a high value neighbourhood. This indicates that the hydrological drought disaster in the region is extremely prominent. In addition, the risk of drought in inland River basins is the highest, followed by the Hai River basin and the Yellow River basin; the Pearl River basin has the lowest. Our results offer insights into the development of drought in China and provide support for disaster risk assessment and response planning.

SPATIAL PATTERN EVOLUTION AND DRIVING FACTORS OF PM2.5 CONCENTRATIONS IN THE GRAND CANAL REGION FROM 2000 TO 2018

Abstract. In recent years, air pollution related to PM2.5 has caused a significant impact on human health. The Grand Canal (GC) is not only a great Cultural heritage created in ancient China but also the longest and largest canal in the world. Based on remotely sensed PM2.5 gridded data in the GC region covering 2000 to 2018, we used the holistic methods of standard deviation ellipse, local moran index, slope trend analysis to reveal the spatiotemporal evolutions of PM2.5 concentrations in the GC regions and investigated the driving factors of PM2.5 concentrations by using the geographically weighted regression (GWR) model. Results show that (1) PM2.5 concentrations in the GC region exhibited an increasing trend and followed by a decreasing trend from 2000 to 2018 (the turning point emerged in 2010). (2) The standard deviation ellipse analyses show that the spatial distributions of PM2.5 concentrations featured more and more concentrated over time, whereas, after the year 2010, the distributions gradually featured scattered. (3) The concentrations of PM2.5 exhibited the strong effects of local spatial autocorrelation and areas with "high-high" agglomeration were mainly located in the central and west regions of the GC region and gradually expanded to the north over time. (4) The areas of regions with rapidly increasing in PM2.5 concentrations gradually decreased over time, however, those with rapidly decreasing in PM2.5 concentrations increased. (5) The influences of the natural factors and socio-economic factors on the distributions of PM2.5 concentrations varied spatially. In detail, the elevation was negatively correlated with PM2.5 concentrations, whereas an opposite relationship between industrial structure and PM2.5 concentrations was observed. The coefficients of rainfall, population density, GDP per capita and foreign investment show different results in positive and negative correlations depending on the position.

High-risk transmission clusters of leprosy in an endemic area in the Northeastern Brazil: A retrospective spatiotemporal modelling (2001-2019).

To analyse and map the leprosy risk areas in the state of Alagoas, an endemic region in the Northeastern Brazil, between 2001 and 2019. Ecological and time series study, using spatial analysis techniques. First, we analyse the epidemiological aspects of leprosy cases, using the data available in the Notifiable Diseases Information System; then, we used the segmented log-linear regression model to assess time trends. Spatial distribution was analysed by the Local Empirical Bayesian Estimator and by calculating the Global and Local Moran Index. Finally, spatiotemporal clusters were identified through scanning statistics, using the Kulldorf method of retrospective analysis. We observed that Alagoas showed an average new case detection rate of 14.43/100,000 inhabitants between 2001 and 2019, being classified as highly endemic. The area of highest risk was the 9th health region (state hinterland), with increasing time trend (Annual Percentage Change/APC = 7.2; p-value < 0.05). Several clusters of high risk of leprosy transmission were verified in Alagoas, including the state capital and hinterland municipalities. Our data indicate that active M.leprae transmission persists in Alagoas; that diagnosis is delayed and that there are high-risk areas, especially in inland municipalities.

Coupling mechanism and development prospect of innovative ecosystem of clean energy in smart agriculture based on blockchain

With the economic development of various countries in the world, the traditional energy supply has been unable to meet the demand of current economic development, and the environmental problems caused by the use of traditional energy are also a major issue that needs to be urgently solved in all countries in the world, so the development and use of clean energy is an important measure to alleviate these two problems. This paper mainly studies the coupling mechanism and development prospect of the innovation ecosystem of clean energy under the smart agriculture based on blockchain. This paper first analyzes the coupling mechanism between the blockchain smart agriculture and the clean energy innovation ecosystem; secondly, it establishes the evaluation index system of the clean energy innovation ecosystem, and measures the development efficiency of the clean innovation ecosystem by using the SBM model, super efficiency SBM model and the global principal component method; thirdly, it uses the development efficiency of the clean energy innovation ecosystem through coupling and coordination The model calculates the coupling coordination of clean energy innovation ecosystem in different regions, and analyzes the spatial-temporal differences, regional differences and spatial agglomeration of the coupling coordination degree. The calculation results show that the average coupling coordination degree of green innovation development in the East, the middle, the West and the whole country is consistent with the development trend of provinces, autonomous regions and cities, showing an upward trend. The results show that the eastern region increases from 0.62 to 0.70; the average coupling coordination degree of the central region is lower than the national average coupling coordination degree, and the distance is gradually increasing; the western region has been in a serious maladjustment level, the global Moran index I value of the coupling coordination degree of clean energy innovation ecosystem is greater than 0, and the Z statistics are greater than the critical value of normal distribution function at the level of 5 %, P values are all less than 0.05 Local Moran index I and Z statistics have been growing steadily, and the significance of spatial positive correlation is getting higher and higher. The research results of this paper can provide a useful reference for the development of clean energy innovation ecosystem under the smart agriculture based on blockchain.

Spatial distribution and molecular epidemiology of Babesia vogeli in household dogs from municipalities with different altitude gradients in the state of Rio de Janeiro, Brazil

We performed a cross-sectional epidemiological study with 456 household dogs from urban and rural areas in two different regions situated at different altitudes in the state of Rio de Janeiro. The PCR technique using 18S rRNA as target revealed prevalence of 7.9% of dogs positive for piroplasmids. These samples were sequenced, and all the sequences were 99.9% to 100% similar to Babesia vogeli sequences from other countries. The spatial distribution of positive cases was analysed using kernel interpolation in the QGIS software, and the spatial correlation indicators among positive dogs, altitude, and presence of ticks were obtained by calculating the local Moran index using the GeoDa software. The spatial correlation between positive cases and altitude was clear based on both visual and statistical observations. Logistic regression applying the Wald method with a cutoff point of 0.1 revealed that dogs from a region with altitude <600 m had a 2.29-fold chance of B. vogeli infection (OR = 2.29; p-value = 0.04; CI: 1.03-5.07), while the rainy season was 2.45 times more associated with B. vogeli infection (OR = 2.45; p-value = 0.01; CI: 1.20-5.01), and dogs infested with Rhipicephalus sanguineus sensu lato had a 2.47 times higher chance of being infected (OR = 2.47; p-value = 0.02; CI: 1.13-5.38). Entropy analysis of the alignment between B. vogeli 18S rRNA (> 1.600 bp) sequences revealed that the most variable region corresponds to the hypervariable V4 region. Genetic homogeneity was observed among the B. vogeli 18S rRNA sequences, with distance values ranging from 0 to 0.007 and a mean value of 0.001. The evolutionary distance (0.003) was greater between the sequences from the municipalities of Barra do Pirai (low altitude) and Teresopolis (high altitude). This study expands the molecular epidemiologic knowledge of B. vogeli and shows points of variability in the B. vogeli 18S rRNA. The results indicate the potential use of spatial analysis tools to improve screening for positive cases, enabling more in-depth studies to strengthen understanding of tick infection prevention in dogs.

Optimizing the Segmentation of a High-Resolution Image by Using a Local Scale Parameter

Image segmentation is a critical procedure in object-based identification and classification of remote sensing data. However, optimal scale-parameter selection presents a challenge, given the presence of complex landscapes and uncertain feature changes. This study proposes a local optimal segmentation approach that considers both intersegment heterogeneity and intrasegment homogeneity, uses the standard deviation and local Moran's index to explore each optimal segment across different scale parameters, and combines the optimal segments into a single layer. The optimal segment is measured by using high-spatial-resolution images. Results show that our approach out-performs and generates less error than the global optimal segmentation approach. The variety of land cover types or intrasegment homogeneity leads to segment matching with the geo-objects on different scales. Local optimal segmentation demonstrates sensitivity to land cover discrepancy and provides good performance on cross-scale segmentation.

Spatial Effect Analysis of Total Factor Productivity and Forestry Economic Growth

This paper takes 31 provinces in China from 2009 to 2018 as the research object. The three-stage data envelopment analysis (DEA) model was used to measure the total factor productivity of forestry, and the entropy method was used to measure the level of economic development and ecological construction. We used the global Moran index to explore the spatial correlation of forestry economic growth, and the local Moran index to explore the spatial agglomeration of forestry economic growth. On this basis, the spatial Durbin model was constructed to explore the spatial spillover effect between forestry total factor productivity and forestry economic growth. The conclusion is as follows: the total factor productivity of forestry in China is increasing continuously, and there are obvious spatial differences. Forestry economic growth has a significant spatial autocorrelation, and an overall upward trend. However, the spatial agglomeration effect was relatively weak and in the beginning stage of its formation. Total factor productivity of forestry has significant direct effect on the growth of forestry economy and forms an indirect spillover effect. Based on this, the countermeasures and suggestions to promote the benign and coordinated development of the forestry economy were put forward.

Total-factor air environmental efficiency and its influencing factors in the areas along the Belt and Road in China: A spatial spillover perspective

In order to promote sustainable economic development in the areas along the Belt and Road in China, it is of great necessity to reduce the negative impact of air pollutants resulting from industrialization and urbanization on the complex and fragile ecological environments of neighboring areas. First, this study estimated the total-factor air environmental efficiency (TFAEE) of 17 provinces along the Belt and Road in China from 2010 to 2017 using a slacks-based measure (SBM) model. Second, the global and local Moran indices were used to test the spatial correlations between TFAEEs. Finally, the spatial factors and spatial spillover effects influencing the TFAEEs were investigated using the spatial Durbin model with spatiotemporal double fixed effects. The results were shown as follows: (1) The total-factor TFAEEs of the areas along the Belt and Road were low and showed significant regional spatial differences during 2010–2017. (2) There was a positive spatial autocorrelation between the TFAEEs of the areas along the Belt and Road, and the spatial distribution generally clustered into High-High and Low-Low concentrations. (3) Economic development and technological innovation played significantly positive effects on TFAEEs of the areas in the Belt and Road, while energy consumption structure had negative effect on it. In addition, although industrial structure and environmental regulation were negatively correlated with TFAEEs, the coefficients were not significant. (4) The positive spatial spillover effect of the TFAEEs of the areas along the Belt and Road was mainly the result of significant environmental regulations and insignificant economic development factors, while the technological innovations, energy consumption structures and industrial structures showed insignificant negative spatial spillover effects.

Spatial analysis and evaluation of road traffic safety performance indexes across the provinces of Turkey from 2015 to 2019

Limited road safety based spatial analysis studies have been conducted in developing countries. Also, little is known about the relationships between province-level road safety performance indexes (RSPIs). Hence, spatial, regression and correlation analysis were used to identify road safety-deficient provinces and determine the relationship between RSPIs. The gathered data comprise 14 RSPIs and nine socioeconomic indicators. Moran’s I and Local Moran indexes were used for conducting the spatial analysis. The natural breaks method was used to cluster similar provinces according to RSPIs. Regarding studied RSPIs, huge local clusters of provinces detected. Eastern provinces had higher road traffic crash (RTC) severity indexes. RTCs were more severe in regions with lower income level. Regions with higher socioeconomic indexes such as population had higher RTC rates. Using RSPIs calculated with distinct exposure measures creates completely different local cluster maps. significant relationships between studied RSPIs were detected. A standard system is needed to organize and categorize the RSPIs. Road safety policies should be region-specific to reduce RSPIs efficiently. Regarding the observed various locations of hot spots in terms of studied RSPIs, further consideration should be given in the process of selecting an RSPI for comparing administrative divisions of a country.

Population-based, spatiotemporal modeling of social risk factors and mortality from schistosomiasis in Brazil between 1999 and 2018

Schistosomiasis remains a significant public health concern in Brazil. To identify areas at, and social determinants of health (SDH) associated with, high-risk for schistosomiasis-related mortality from Brazil, we conducted a spatial and spatiotemporal modeling assessing all deaths confirmed in Brazil between 1999 and 2018. We used the segmented log-linear regression model to assess temporal trends, and the local empirical Bayesian estimator, the Global and Local Moran Index for spatial analysis. A total of 12,251 schistosomiasis-related deaths were reported in this period. Within the Mortality Information System (SIM) of the Brazilian Ministry of Health, the states of Alagoas (AL), Pernambuco (PE) and Sergipe (SE) recording the highest mortality rates: 2.21, 1.92 and 0.80 deaths/100,000 inhabitants, respectively. Analyses revealed an increase in the mean age of schistosomiasis-related deaths over the time assessed (APC = 0.9; p-value<0.05). Spatial analysis identified a concentration of municipalities presenting high risk of schistosomiasis-related mortality along the coastline of PE and AL. Similarly, we identified the formation of high space-time clusters in municipalities in the states of PE, AL, SE, Bahia, and Minas Gerais. Finally, mortality rates showed a significant correlation with 96.96% of SDH indices. The data reveal additional important changes in schistosomiasis-related deaths in Brazil between 1999 and 2018, such as a slow reduction among males (unlike females that displayed no change). Regardless, our analyses indicates that schistosomiasis continues to have the greatest detrimental impact in poor regions of Brazil and suggest the need for enhancement of current control measures to accelerate progress.

Local Moran Index: an application in epidemiological coefficients of the COVID-19 pandemic in Brazil

The COVID-19 pandemic spread quickly around the world in a frightening way. In Brazil, the third country in the world with the highest number of people infected and killed by the disease, it is important that the government health authorities identify the federation units that stand out in cases and deaths due to this disease for targeting resources. The Local Moran Index is a statistical tool that estimates those units of the federation that stands out the most with some statistical significance. We used the epidemiological coefficients of incidence, prevalence, and lethality to describe Brazil’s pandemic better today. We use R software to obtain maps and results.

Evaluating Social Distancing Measures and Their Association with the Covid-19 Pandemic in South America

Social distancing is a powerful non-pharmaceutical intervention used as a way to slow the spread of the SARS-CoV-2 virus around the world since the end of 2019 in China. Taking that into account, this work aimed to identify variations on population mobility in South America during the pandemic (15 February to 27 October 2020). We used a data-driven approach to create a community mobility index from the Google Covid-19 Community Mobility and relate it to the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT). Two hypotheses were established: countries which have adopted stricter social distancing measures have also a lower level of circulation (H1), and mobility is occurring randomly in space (H2). Considering a transient period, a low capacity of governments to respond to the pandemic with more stringent measures of social distancing was observed at the beginning of the crisis. In turn, considering a steady-state period, the results showed an inverse relationship between the Covid stringency index and the community mobility index for at least three countries (H1 rejected). Regarding the spatial analysis, global and local Moran indices revealed regional mobility patterns for Argentina, Brazil, and Chile (H1 rejected). In Brazil, the absence of coordinated policies between the federal government and states regarding social distancing may have played an important role for several and extensive clusters formation. On the other hand, the results for Argentina and Chile could be signals for the difficulties of governments in keeping their population under control, and for long periods, even under stricter decrees.

Space-time risk cluster of visceral leishmaniasis in Brazilian endemic region with high social vulnerability: An ecological time series study

Despite visceral leishmaniasis (VL) being epidemic in most Brazilian regions, the Northeast region is responsible for the highest morbidity and mortality outcomes within the country. To analyse the spatiotemporal dynamics of VL cases to identify the temporal trends and high-risk areas for VL transmission, as well as the association of the disease with social vulnerability in Brazilian Northeast. We carried out an ecological time series study employing spatial analysis techniques using all VL confirmed cases of 1,794 municipalities of Brazilian Northeast between the years 2000 to 2017. The Social Vulnerability Index (SVI) was used to represent the social vulnerability. Incidence rates were standardized and smoothed by the Local Empirical Bayesian Method. Time trends were examined through segmented linear regression. Spatiotemporal analysis consisted of uni- and bivariate Global and Local Moran indexes and space-time scan statistics. Incidence rate remained stable and ranged from 4.84 to 3.52 cases/100,000 inhabitants. There was higher case prevalence between males (62.71%), children and adolescents (63.27%), non-white (69.75%) and urban residents (62.58%). Increasing trends of new cases were observed among adult male subjects (≥ 40 years old) and urban residents. Importantly, VL incidence showed a direct spatial dependence. Spatial and space-time clusters were identified in sertão and meio-norte sub-regions, overlapping with high social vulnerability areas. VL is a persistent health issue in Brazilian Northeast and associated with social vulnerability. Space-time clustering of VL cases in socially vulnerable municipalities demands intersectoral public policies of surveillance and control, with focus on reducing inequalities and improving living conditions for regional inhabitants.

Investigating the spatiotemporal variability and driving factors of China's building embodied carbon emissions.

Rapid and large scale construction activities consume significant resources and make impacts on the environment. To support policy for emission reduction and route of low-carbon society development, this paper estimated building stocks and building embodied carbon emissions (BECEs) in China's 31 provinces from 1997 to 2016 by material flow analysis (MFA). Furthermore, global and local Moran's indices were employed to investigate the geographical clustering patterns, and temporal and spatial decomposition models were proposed to identify the driving forces. The results reveal the total BECEs has boomed from 9.67 billion tons in 1997 to 28.99 billion tons in 2016. BECEs in 31 provinces have experienced consistent increase but obvious differences in growth rate, and are spatially inclined to decrease from eastern coastal regions to western inland regions. The change of spatial agglomeration pattern is complex and variable. It presents that a long and narrow "H-L agglomeration" is located in the two northernmost provinces and the other 29 provinces enforce a sequence arrangement with an order of "H-H", "L-H", "H-L", and "L-L" from east to west. Temporal decomposition results show that investment scale, economic level, and population density are the main driving forces for the increase of BECEs from both national and provincial levels, while the main reasons for the decrease are technical level and return on investment. Spatial decomposition results demonstrate that population density and provincial area are the main driving forces for the difference between provincial and national average, and others cause the difference among provinces.

Changes in the territorial distribution of population and job seekers with higher education in Hungary

The study uses different statistical methods to examine the change in the territorial distribution and concentration of the tertiary-educated population and the unemployed between 2001 and 2019. Among the known regional inequality indicators measuring regional disparities, the Dual index and the Hoover index were used. With the help of the location quotient (LQ), the representation of those with higher education in relation to the average was explored and represented at the settlement level. Territorial autocorrelation was performed to more complexly examine the distribution and concentration of tertiary graduates within the population and the unemployed. Spatial neighborhood impact was analyzed using the Global and Local Moran Index. The autocorrelation confirmed regular spatial arrangements in the spatial distribution of tertiary graduates. The study explores whether different weighting of raw data affects the result of autocorrelation, furthermore determines the differences between the location quotient and the GIS representation of Local Moran I.