GeoDa Software Research Articles

Determining the Leaf Area Index and Percentage of Area Covered by Coffee Crops Using UAV RGB Images

Leaf area is a component of crop growth and yield prediction models. Few studies have used the structure from motion (SfM) algorithm, which is based on the principles of traditional stereophotogrammetry, to obtain the leaf area index (LAI). Thus, the objective of this study was to follow the evolution of the LAI and percentage of land cover (%COV) in coffee plants, using pre-established equations and plant measurements obtained from generated 3-D point clouds, combined with the application of the SfM algorithm to digital images recorded by a camera coupled to an unmanned aerial vehicle (UAV). The experiment was conducted in a coffee plantation located in southeastern Brazil. A rotary wing UAV containing a conventional camera was used. The images were collected once per month for 12 months. Image processing was performed using PhotoScan software. Regression analysis and spatial analysis were performed using R and GeoDa software, respectively. The resulting %COV data had R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and RMSE values of 89% and 3.41, respectively, while those for LAI had R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and RMSE of 88% and 0.47, respectively. Significant %COV results were obtained in the months of January, February, and March of 2018. There was significant autocorrelation for the LAI values from January to May 2018, with most blocks in the central and center-west regions presenting LAI values > 3.0. It was possible to monitor the temporal and spatial behavior of the LAI and %COV, allowing for the conclusion that this methodology generated results that are consistent with the literature.

Research on Ecological Compensation Standard and Spatial Optimization of the Five Major River Basins in Jiangxi Province

Based on the actual situation of the river basin in Jiangxi Province and the related literature, we construct an estimation model for the ecological compensation standards of the Fiver Major River Basins in Jiangxi Province, and have measured the ecological compensation standard of each research area. We use ArcGis and Geoda software to analyze spatial autocorrelation and hotspots of ecological compensation standards. The above research results are used as the basis for spatial optimization of watershed ecological compensation standards, contributing to the further improvement of ecological compensation ofthe Five Major River Basins in Jiangxi Province.

Yield Variability Assessment in Paddy Crop using Automatic Yield Monitoring System

A study was conducted to assess yield variability of paddy crop using automatic yield monitoring (AYM) system fitted on an indigenous grain combine harvester. Algorithms were developed to assess the on-farm yield variability and classify the field in different yield zones. Yield maps were created using ArcGIS and open source GeoDa software for four years (2014, 2015, 2016 and 2017). The calibration factor (CF) of AYM system for paddy crop was 6.79. Yield variations were recorded in one ha plot, and classified in five different yield zones of&lt;1900 kg.ha-1, 2000-2999 kg.ha-1, 3000-3999 kg.ha-1, 4000-4999 kg.ha-1, and &gt;5000 kg.ha-1. Variation in actual yield and yield measured by automatic yield monitor varied from (+) 2.8 to (+)5.1 per cent. Maximum temporal variability (14.7 %) was observed between the year 2014 and 2017. The AYM system could show the real-time temporal and spatial variability classification and quantification in paddy field.

Spatial Effects of Environmental Regulation and Green Credits on Green Technology Innovation Under Low-Carbon Economy Background Conditions.

Based on the data of green credit (GC), environmental regulation (ER) and green technology innovation (GTI) in 30 provinces and cities of China from 2007 to 2016, this study investigated the relationship between green credit and green technology innovation development and analyzed the adjustment effect of ER on GC to promote GTI using Geoda and Matlab2016 software, so as to further guide and encourage GC. The results show that GTI in 30 provinces and municipalities in China has a significant spatial agglomeration effect. Single GC plays a certain role in promoting local technology innovation, but it fails to influences the surrounding areas. Environmental regulation has a certain regulatory effect on the relationship between green credit and green technology innovation in the province but also fails to influences the surrounding areas.

How to Maintain a Sustainable Environment? A Spatial Evolution of Urban Atmospheric Pollution and Impact Factors in China

Urban pollution has significantly contributed to the spread of diseases and global warming. The analysis of spatial distribution characteristics of atmospheric pollutants is crucial for making sustainable industrial policy, and environmentally friendly urban planning. In this paper, GeoDa software is used to analyze how sulfur dioxide (SO2), nitrogen oxides (NOx), and smoke dust (DUS) are spatially distributed in various provinces of China. Then, global spatial correlation test and cluster analysis are carried out to obtain the spatial evolution characteristics of three pollutants. Afterward, the spatial panel data model is applied to explore the factors that affect the spatial evolution of SO2, NOx and smoke dust (DUS) nationwide. MATLAB is used to estimate the Spatial Lag Model (SLM) and the Spatial Error Model (SEM) of the three pollutants, respectively. According to our analysis, SEM is more applicable for SO2 and NOx, whereas SLM is optimal for smoke dust (DUS). The results show that foreign direct investment (FDI), industrial structure, and urbanization aggravate environmental pollution, while per capita gross domestic products (per capita GDP) has a negative relationship with the cluster of pollutants. The study concludes by informing public policy makers on environment friendly policies for a more sustainable development.

Methods to compare the spatial variability of UAV-based spectral and geometric information with ground autocorrelated data. A case of study for precision viticulture

One of the key steps that would lead winegrowers to implement precision viticulture as a management tool would be the clear demonstration of the agronomic and oenological significance of the zones delineated within a vineyard based, totally or partially, on remote-acquired information. To perform this analysis, it is necessary to compare image-derived variables to crop characteristics. Classical ordinary least square (OLS) regression is not well suit for spatially structured data, while Moran’s index (MI) and local indicators of spatial autocorrelation (LISA) take autocorrelation into account. The aim of this work was to evaluate the performance of statistical methods to compare different maps of a vineyard, some including variables derived from UAV acquired imagery, and some from in situ ground characterization. The study was conducted during 2015 and 2016 seasons in an adult 7.5 ha cv. ‘Tempranillo’ vineyard located in Traibuenas, Navarra, Spain. The maps obtained out of UAV-imagery, volume index (VI) and normalized difference vegetation index (NDVI) were compared to the maps obtained for the agronomic variables measured (yield, berry weight and total soluble solids). The bivariate MI and the bivariate LISA cluster map obtained using Geoda software indicate depict the spatial cluster association between variables in 2015 and 2016 with different types of local spatial autocorrelation. The use of these methods that take into account data spatial structure, to compare ground autocorrelated data and spectral and geometric information derived from UAV-acquired imagery has been proved to be highly necessary and advisable.

EPIDEMIOLOGICAL BEHAVIOR OF LEPROSY IN THE STATE OF MATO GROSSO: A GEOSPATIAL APPROACH

Objectives: To analyze the epidemiological pattern of leprosy in the state of Mato Grosso from 2007 to 2011 from a geospatial analysis. Method: ecological, descriptive-transversal study, performed from SINAN, TABNET, DATASUS and GEODA software version 0.9.5 Spatial Analysis Laboratory, University of Illinois at Urbana-Champaign, USA. Results: Of the 13,648 reports, 6,583 (48.23%) were in the age range of 40 to 69 years. The state surpassed 16 cases/10 000 thousand inhabitants. The indicator younger than 15 years showed positive autocorrelation (I = 0.220711 p = 0.001). The illiteracy rate (p = 0.011) and the Gini Coefficient (p = 0.020) were predictive factors in the disease. Conclusion: The results demonstrate the endemic character of leprosy in the State of Mato Grosso, which reinforces the importance of prevention actions, timely diagnosis, adequate treatment and continuous monitoring of this disease.

Operational Local Join Count Statistics for Cluster Detection.

This paper operationalizes the idea of a local indicator of spatial association (LISA) for the situation where the variables of interest are binary. This yields a conditional version of a local join count statistic. The statistic is extended to a bivariate and multivariate context, with an explicit treatment of co-location. The approach provides an alternative to point pattern based statistics for situations where all potential locations of an event are available (e.g., all parcels in a city). The statistics are implemented in the open source GeoDa software and yield maps of local clusters of binary variables, as well as co-location clusters of two (or more) binary variables. Empirical illustrations investigate local clusters of house sales in Detroit in 2013 and 2014, and urban design characteristics of Chicago census blocks in 2017.

Spatial Distribution of Anemia Among Adolescents In Banda Aceh, Indonesia

Adolescents are an important assets to determine whether a country will be advance in the future. A good nutrient intake is needed to ensure their growth and development. Lack of nutrients will cause various health problems, one of them is anemia. Anemia is influenced by many factors, including spatial factors. The authors examine the relationship between anemia and variables that affect it. The relationship may be influenced by regional or spatial factors. Total data 602 of adolescents in nine sub-districts in Banda Aceh were analyzed using GeoDa software. The results of this study revealed that the number of cases of anemia in adolescents in Banda Aceh is no correlated with nutritional status, nutrient intake, breakfast habits, anemia knowledge, parental income, and the number of family covered, except in Kuta Alam and Lueng Bata Sub-districts. The numbers of cases of anemia in adolescents in Kuta Alam Sub-district are more correlated with nutritional status, nutrient intake, breakfast habits, anemia knowledges, parent’s income, and numbers of family covered. The number of cases of anemia in adolescents in Lueng Bata Sub-district is more correlated with nutritional status, and the number of family covered. Thus, for nutrient intake, anemia knowledge and parental income, they are no correlated with the number of anemia cases.

Human Capital Spillovers and Human Development Index in Yogyakarta Special Region and Central Java

This study analyzes the spillover of human capital in the Yogyakarta Special Region and Central Java. The research data includes the Human Development Index (HDI), capital, the ratio of college graduate workers, and the number of medical personnel in 2017 published by the Central Bureau of Statistics (BPS). This study uses a Spatial Regression analysis that includes elements of human capital spillover estimated by Geoda software. The results showed that there was a spillover of human capital between regencies/cities in Yogyakarta Special Region and Central Java. Lambda coefficient value, in Spatial Error Model (SEM), amounting to 0.5074 indicates that the Human Development Index (HDI) score of each regency/city will get an influence of 0.5074 multiplied by the HDI value of the neighboring regency/city. The capital variable is significant and exhibits that a rise in the HDI score will follow an increase in the number of capital. However, the ratio of college-educated worker and medical personnel is not significant. Due to the linkages between these provinces, the regency government and the provincial government should synergize each other, especially in the formulation of economic policies.

Spatial distribution of dengue fever incidence in Kerala state - A retrospective study

Background: Dengue fever is a rapidly spreading viral infection. The incidence of dengue fever cases in Kerala is still rising from the year 2006 onward in spite of several measures. The pattern of dengue fever distribution is not uniform in all the districts. Only some districts are reporting a high number of cases in every year. Based on this background, the present study conducted to observe and analyze the spatial distribution pattern of dengue fever incidence in Kerala state. Objectives: The objectives of the present study are to assess the spatial auto-correlation of dengue fever incidence in Kerala state and to identify the significant spatial clusters having a high or low incidence of dengue fever in Kerala state. Materials and Methods: The present study is a retrospective observational study using the secondary data of Kerala state. Year wise incidence of dengue fever cases in each district was measured. Since 2011, hierarchical maps of the incidence of dengue fever in different districts were created using QGIS software. Spatial auto-correlation of dengue fever incidence in every year was tested using GeoDa software. Global Moran’s i value was measured in each year, local indicators of spatial association maps prepared to observe the spatial distribution of dengue fever incidence in Kerala. Results: Incidence of dengue fever cases is in increasing trend in every district, but in the year 2014 and 2018, the incidence was decreased compared to the previous year. Among all the years, 2013, 2016, and 2017 showed significant positive global spatial auto-correlation. Kollam district is showed a high-high spatial pattern. Low-low cluster areas were observed in North Kerala from 2011 to 2013 and in central Kerala in the years 2016 and 2018. Conclusion: Spatial distribution of dengue fever incidence is not uniform every year. Kollam district significantly reporting the high incidence and surrounded by high incidence districts. Kannur, Malappuram, Palakkad, Wayanad, Thrissur, and Ernakulam districts significantly reporting the low incidence of dengue fever and they were surrounded by low incidence districts. This type of analysis helps identify areas where control efforts can be intensified strategically thus optimizing resources Larger studies can be conducted at subnation or nation level, to eliminate the border issues between states and for better identification of local spatial clusters of dengue fever incidence.

Spatial distribution of dentists in Thailand

Background: Thailand has been facing maldistribution of dentists for decades despite its continuous increase in total numbers. Aims: The objective of this study was to analyze spatial inequality of dentist distribution patterns in Thailand. Materials and Methods: Data on dentist in public sector profiles between 2007 and 2015 were retrieved from the Ministry of Public Health, Thailand, to analyze the distribution patterns and spatial relationships. The distribution of dentists was visualized on a map using Quantum Geographic Information System. Global Moran I and local indicators of spatial association were analyzed using GeoDa software to determine the distribution of spatial relationship. Results: The total number of dentists has been increased annually from 2007 to 2015. Initially, the high–high (population-to-dentist ratio) spatial clusters were common in the Northeast (NE) region, whereas there were many low–low spatial clusters in Central region. In 2015, the high–high spatial clusters persisted in the NE region, but the low–low spatial clusters were hardly found in the Central region. Conclusions: Unequal geographical distribution of dentist still existed in Thailand, especially in the NE region. Both supply side concerning administrative factors related to resource allocation, and demand side of socioeconomic development and population changes from migration had influences on inequality in dentist distribution.

Yield Variability Assessment in Paddy Crop using Automatic Yield Monitoring System

A study was conducted to assess yield variability of paddy crop using automatic yield monitoring (AYM) system fitted on an indigenous grain combine harvester. Algorithms were developed to assess the on-farm yield variability and classify the field in different yield zones. Yield maps were created using ArcGIS and open source GeoDa software for four years (2014, 2015, 2016 and 2017). The calibration factor (CF) of AYM system for paddy crop was 6.79. Yield variations were recorded in one ha plot, and classified in five different yield zones of&lt;1900 kg.ha-1, 2000-2999 kg.ha-1, 3000-3999 kg.ha-1, 4000-4999 kg.ha-1, and &gt;5000 kg.ha-1. Variation in actual yield and yield measured by automatic yield monitor varied from (+) 2.8 to (+)5.1 per cent. Maximum temporal variability (14.7 %) was observed between the year 2014 and 2017. The AYM system could show the real-time temporal and spatial variability classification and quantification in paddy field.

Spatial analysis of dengue, cysticercosis and Chagas disease mortality in Ecuador, 2011-2016.

Neglected tropical diseases (NTDs) continue to be an important cause of disability and mortality in the poorest tropical and subtropical areas. This is an ecological study. We included all death certificates with dengue, cysticercosis and Chagas disease in Ecuador from 2011 to 2016. The spatial autocorrelation was evaluated by GeoDa software through the Global Moran's I index and the formation of clusters by the local index of spatial association. The Global Moran's I index for the study period shows a positive spatial autocorrelation for dengue, cysticercosis and Chagas disease (0.25, p=0.001; 0.07, p=0.04; 0.45, p=0.001, respectively). The clusters identified as higher risk in the country could be targeted by policymakers to adequately develop strategies to strengthen health promotion policies that break the cycle of these diseases.

Spatial clusters of daytime sleepiness and association with nighttime noise levels in a Swiss general population (GeoHypnoLaus).

Daytime sleepiness is highly prevalent in the general adult population and has been linked to an increased risk of workplace and vehicle accidents, lower professional performance and poorer health. Despite the established relationship between noise and daytime sleepiness, little research has explored the individual-level spatial distribution of noise-related sleep disturbances. We assessed the spatial dependence of daytime sleepiness and tested whether clusters of individuals exhibiting higher daytime sleepiness were characterized by higher nocturnal noise levels than other clusters. Population-based cross-sectional study, in the city of Lausanne, Switzerland. Sleepiness was measured using the Epworth Sleepiness Scale (ESS) for 3697 georeferenced individuals from the CoLaus|PsyCoLaus cohort (period = 2009-2012). We used the sonBASE georeferenced database produced by the Swiss Federal Office for the Environment to characterize nighttime road traffic noise exposure throughout the city. We used the GeoDa software program to calculate the Getis-Ord Gi* statistics for unadjusted and adjusted ESS in order to detect spatial clusters of high and low ESS values. Modeled nighttime noise exposure from road and rail traffic was compared across ESS clusters. Daytime sleepiness was not randomly distributed and showed a significant spatial dependence. The median nighttime traffic noise exposure was significantly different across the three ESS Getis cluster classes (p < 0.001). The mean nighttime noise exposure in the high ESS cluster class was 47.6, dB(A) 5.2 dB(A) higher than in low clusters (p < 0.001) and 2.1 dB(A) higher than in the neutral class (p < 0.001). These associations were independent of major potential confounders including body mass index and neighborhood income level. Clusters of higher daytime sleepiness in adults are associated with higher median nighttime noise levels. The identification of these clusters can guide tailored public health interventions.

ANALISIS PDRB SEKTOR INDUSTRI DENGAN PENDEKATAN REGRESI SPASIAL: STUDI KASUS INDONESIA 2011-2015

Spatial analysis of econometric GRDP of industrial sector is one of regional econometrics study in an effort to define economic phenomenon which many developed by economic theories into mathematical form. This study aims to determine whether there is spatial influence between provinces in Indonesia, and how the influence of labor and investment variables to GRDP of industry sector in Indonesia. This study uses panel data from 2011-2015 which averaged and then processed using Geoda software, and the selected model is Spatial Error Model (SEM). The result shows that there is spatial inter-provincial relation with negative direction, which means that if there is an increase of GRDP of industry in study area will decrease GRDP of industry in neighboring region. Labor variable has a significant positive effect on the GRDP of industry, which means that if there is an increase in manpower will increase the GRDP of industry. Investment variable has positive significant positive effect, which means that if there is an increase of investment it will increase the GRDP of industry. In brief, this research can help the government to establish policies on the industrial sector and other economic sectors by considering the spatial aspects .

Demographic and spatial disparity in HIV prevalence among incarcerated population in the US: A state-level analysis.

The purpose of this study was to explore empirically the presence of any spatial and demographic disparity in the Human Immunodeficiency Virus (HIV) infection rate among the prison inmates across 48 states in the US and compare the results for 2000 and 2010. HIV infection is a severe health issue for incarcerated populations in the US. In 2010, the rate of diagnosed HIV infection among inmates in state and federal prisons was five times more than the nonincarcerated population. The National Prisoner Statistics database was used to find the demographic disparities in HIV prevalence rate based on incarceration rate, gender, race/ethnicity, the proportion of non-US citizens, and proportion of population below 18 years. State-level spatial mapping, Pearson correlation coefficient, and Moran's I statistic (univariate and bivariate) were computed based on these demographic characteristics using QGIS and Geoda software. There was a statistically significant pattern of spatial disparity in overall, male and female HIV infection rates across the state prisoners, with South and South-Eastern states facing a higher risk of infection. There was also statistically significant bivariate spatial association of HIV infection rate with the covariates: whites (negative), blacks (positive), non-US citizen (positive), and prisoners under age 18 years (positive) for both 2000 and 2010. There was a statistically significant higher HIV infection rate among the female prisoners in comparison to the male prisoners. It is of prime importance to examine the state-level disparities in HIV infection rate based on place and demographics. This is because evaluating the spatial pattern will help in accessing the relevant local information and provide federal agencies with better knowledge to target interventions and prevention programs toward the subgroup of the population at higher risk and help in controlling and reducing HIV infection prevalence.

Measuring Spatial Cluster for Leading Industries in Surakarta with Exploratory Spatial Data Analysis (ESDA)

Economic clusters are significant to support the economic growth, particularly at regional scale. The approach in the analysis has evolved from the emphasis on the comparison between the intra and extra regional into the spatial approach that is capable to detect the prevailing movement and concentration pattern in particular economic activity, hence the generated data is more informative and analyzable. This paper concentrates in identifying the location and assessing the economic clusters of leading industries in Surakarta City, Indonesia based on the number of units and labor absorption by using the Exploratory Spatial Data Analysis (ESDA). In association with the first objective, ArcGis was employed to find out how the concentration of leading industries in Surakarta was formed. The analysis revealed that the industries in Surakarta City have a propensity to be remote from downtown and concentrated in the northern part of the city. The second objective was revealed by performing the Moran’s index on GeoDa software to determine the spatial autocorrelation among the observed areas as the basis in finding the leading industrial cluster. The analysis indicated that all leading industries have relatively low Moran’s index meaning there was no dominant leading industry in Surakarta. These results have been confirmed by the LISA method to reveal the areas having spatial autocorrelation for each industrial sector.

Spatial and temporal characteristics of HIV/AIDS in Hubei province, 2010-2013

Objective: To understand the spatial and temporal distribution of HIV/AIDS in Hubei province, and provide scientific evidence for the prevention and control of AIDS. Methods: GeoDa software was used for autocorrelation analysis, SatScan 9.2 software was used for statistical analysis of spatial scanning, and finally geographic information system was used for visualization. Results: A total of 6 952 HIV/AIDS cases were reported during 2010-2013 in Hubei, and the spatial autocorrelation analysis showed that Global Moran's I index was 0.266 (P<0.05), indicating that there was a positive spatial autocorrelation of HIV/AIDS. Global Moran's I index increased year by year (P<0.05), indicating that the increased spatial aggregation of HIV/AIDS during 2010-2013. The local Moran's I index showed that " high-high" clustering areas were in Wuhan, and the number of " high-high" clustering areas increased during 2010-2013. Moreover, the " high-high" clustering areas expanded from Wuhan to surrounding areas. Spatial and temporal scan analysis revealed that 19 counties in Wuhan, Huangshi, Ezhou, Xianning with a radius of 60.01 km (LLR=625.14, RR=3.23) were the main spatial and temporal clustering area during 2012-2013. Conclusion: The spatial changes of HIV/AIDS seemed to be regular from 2010 to 2013 in Wuhan, spatial correlation at provincial level decreased and the " high-high" clustering areas gradually expanded from Wuhan to surrounding areas, indicating that it is necessary to strengthen the AIDS prevention and control in these areas in Hubei.

Inequalities in tobacco outlet density by race, ethnicity and socioeconomic status, 2012, USA: results from the ASPiRE Study.

Evidence of racial/ethnic inequalities in tobacco outlet density is limited by: (1) reliance on studies from single counties or states, (2) limited attention to spatial dependence, and (3) an unclear theory-based relationship between neighbourhood composition and tobacco outlet density. In 97 counties from the contiguous USA, we calculated the 2012 density of likely tobacco outlets (N=90 407), defined as tobacco outlets per 1000 population in census tracts (n=17 667). We used 2 spatial regression techniques, (1) a spatial errors approach in GeoDa software and (2) fitting a covariance function to the errors using a distance matrix of all tract centroids. We examined density as a function of race, ethnicity, income and 2 indicators identified from city planning literature to indicate neighbourhood stability (vacant housing, renter-occupied housing). The average density was 1.3 tobacco outlets per 1000 persons. Both spatial regression approaches yielded similar results. In unadjusted models, tobacco outlet density was positively associated with the proportion of black residents and negatively associated with the proportion of Asian residents, white residents and median household income. There was no association with the proportion of Hispanic residents. Indicators of neighbourhood stability explained the disproportionate density associated with black residential composition, but inequalities by income persisted in multivariable models. Data from a large sample of US counties and results from 2 techniques to address spatial dependence strengthen evidence of inequalities in tobacco outlet density by race and income. Further research is needed to understand the underlying mechanisms in order to strengthen interventions.