scholarly journals 1011. Geospatial Analysis of Antibiotic Susceptibility in Wisconsin

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S596-S597
Author(s):  
Laurel Legenza ◽  
Kyle McNair ◽  
James P Lacy ◽  
Song Gao ◽  
Warren Rose
Keyword(s):  
Rural Areas ◽  
Antibiotic Susceptibility ◽  
Hot Spot ◽  
Moran’S I ◽  
Research Support ◽  
Moran's I ◽  
E Coli ◽  
Index Analysis ◽  
Block Groups ◽  
Spot Analysis

Abstract Background The global threat of antimicrobial resistance (AMR) varies regionally. Regional differences may be related to socio-economic factors such as the Area Deprivation Index (ADI) score. Our hypothesis is that AMR spatial distribution is not random. Methods Patient level antibiotic susceptibility data was collected from three regionally distinct Wisconsin health systems (UW Health, Fort HealthCare, Marshfield Clinic Health System [MCHS]). Patient addresses were geocoded to coordinates and joined with US Census Block Groups. For each culture source, we included the initial E. coli isolate per patient per year with a patient address in Wisconsin. Percent susceptibility was calculated by block group. Spatial autocorrelation was determined by Global Moran’s I, which quantifies the attribute being analyzed as spatially dispersed, randomly distributed, or clustered by a range of −1 to +1. Linear regression correlated ADI to susceptibility. Hot spot analysis identified blocks with statistically significant higher and lower susceptibility (Figure 1). Figure 1. Geographic example of hot spot analysis and interpretation. Results The UW Health results included more urban areas, more block groups and greater isolate geographic density (n = 44,629 E. coli, 2009-2018), compared to Fort HealthCare (n = 6,065 isolates, 2012-2018) and MCHS (50,405 isolates, 2009-2018). A positive spatially clustered pattern was identified from the UW Health data for ciprofloxacin (Moran’s I = 0.096, p = 0.005) and trimethoprim/sulfamethoxazole (TMP/SMX) susceptibility (Moran’s I = 0.180, p < 0.001; Figures 2-3). Fort HealthCare and MCHS distribution was likely random for TMP/SMX and ciprofloxacin by Moran’s I. Linear regression of ADI (scale 1-10, least to most disadvantaged) and susceptibility did not find significance, but susceptibility was lower in more disadvantaged block groups. At the local level, we identified hot and cold spots with 90%, 95%, and 99% confidence, with more hot spots in rural regions. Figure 2. Results from Moran’s Index analysis identifying geographically clustered ciprofloxacin susceptibility results. Figure 3. Results from Moran’s Index analysis identifying geographically clustered sulfamethoxazole/trimethoprim susceptibility results. Conclusion Overall, Moran’s I analysis is more able to identify a clustered pattern in urban versus rural areas. Yet, the local hot spot results indicate that variations in antibiotic susceptibility may be more common in rural areas. The results are limited to data from patients with access to the health systems included. Disclosures Warren Rose, PharmD, MPH, Merck (Grant/Research Support)Paratek (Grant/Research Support, Advisor or Review Panel member)

scholarly journals Geospatial patterns of comorbidity prevalence among people with osteoarthritis in Alberta Canada

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Xiaoxiao Liu ◽  
Rizwan Shahid ◽  
Alka B. Patel ◽  
Terrence McDonald ◽  
Stefania Bertazzon ◽  
...  
Keyword(s):  
First Nations ◽  
Rural Areas ◽  
Urban Areas ◽  
Hot Spot ◽  
Local Level ◽  
Moran’S I ◽  
Local Health ◽  
Case Definitions ◽  
Moran's I ◽  
Remote Areas

Abstract Background Knowledge of geospatial pattern in comorbidities prevalence is critical to an understanding of the local health needs among people with osteoarthritis (OA). It provides valuable information for targeting optimal OA treatment and management at the local level. However, there is, at present, limited evidence about the geospatial pattern of comorbidity prevalence in Alberta, Canada. Methods Five administrative health datasets were linked to identify OA cases and comorbidities using validated case definitions. We explored the geospatial pattern in comorbidity prevalence at two standard geographic areas levels defined by the Alberta Health Services: descriptive analysis at rural-urban continuum level; spatial analysis (global Moran’s I, hot spot analysis, cluster and outlier analysis) at the local geographic area (LGA) level. We compared area-level indicators in comorbidities hotspots to those in the rest of Alberta (non-hotspots). Results Among 359,638 OA cases in 2013, approximately 60% of people resided in Metro and Urban areas, compared to 2% in Rural Remote areas. All comorbidity groups exhibited statistically significant spatial autocorrelation (hypertension: Moran’s I index 0.24, z score 4.61). Comorbidity hotspots, except depression, were located primarily in Rural and Rural Remote areas. Depression was more prevalent in Metro (Edmonton-Abbottsfield: 194 cases per 1000 population, 95%CI 192–195) and Urban LGAs (Lethbridge-North: 169, 95%CI 168–171) compared to Rural areas (Fox Creek: 65, 95%CI 63–68). Comorbidities hotspots included a higher percentage of First Nations or Inuit people. People with OA living in hotspots had lower socioeconomic status and less access to care compared to non-hotspots. Conclusions The findings highlight notable rural-urban disparities in comorbidities prevalence among people with OA in Alberta, Canada. Our study provides valuable evidence for policy and decision makers to design programs that ensure patients with OA receive optimal health management tailored to their local needs and a reduction in current OA health disparities.

scholarly journals Spatial Analysis of COVID-19 and Exploration of Its Environmental and Socio-Demographic Risk Factors Using Spatial Statistical Methods: A Case Study of Iran

2020 ◽  
Vol 5 (3) ◽  
pp. 145-154
Author(s):  
Mohsen Shariati ◽  
◽  
Mahsa Jahangiri-rad ◽  
Fatima Mahmud Muhammad ◽  
Jafar Shariati ◽  
...  
Keyword(s):  
Risk Factors ◽  
Spatial Autocorrelation ◽  
Confidence Level ◽  
Hot Spot ◽  
Moran’S I ◽  
Moran's I ◽  
Hot Spot Analysis ◽  
The Relationship ◽  
Global Moran’S I ◽  
Spot Analysis

Background: Iran detected its first COVID-19 case in February 2020 in Qom province, which rapidly spread to other cities in the country. Iran, as one of those countries with the highest number of infected people, has officially reported 1812 deaths from a total number of 23049 confirmed infected cases that we used in the analysis. Materials and Methods: Geographic distribution by the map of calculated incidence rates for COVID -19 in Iran within the period was prepared by GIS 10.6 Spatial autocorrelation (Global Moran’s I) and hot spot analysis were used to assess COVID -19 spatial patterns. The ordinary least square method was used to estimate the relationship between COVID -19 and the risk factors. The next step was to explore Geographically Weighted Regression (GWR) models that might better explain the variation in COVID -19 cases based on the environmental and socio-demographic factors. Results: The spatial autocorrelation (Global Moran’s I) result showed that COVID-19 cases in the studied area were in clustered patterns. For statistically significant positive z-scores, the larger the z-score is, the more intense the clustering of high values (hot spot), such as Semnan, Qom, Isfahan, Mazandaran, Alborz, and Tehran. Hot spot analysis detected clustering of a hot spot with confidence level 99% for Semnan, Qom, Isfahan, Mazandaran, Alborz, and Tehran, as well. The risk factors were removed from the model step by step. Finally, just the distance from the epicenter was adopted in the model. GWR efforts increased the explanatory value of risk factor with better special precision (adjusted R-squared=0.44) Conclusion: The highest CIR was concentrated around Qom. Also, the greater the distance from the center of prevalence (Qom), the fewer the patients. Hot spot analysis also implies that the neighboring provinces of prevalence centers exhibited hot spots with a 99% confidence level. Furthermore, the results of OLS analysis showed the significant correlation of CIR is with the distance from epicenter (Qom). The GWR can result in the spatial granularity providing an opportunity to well understand the relationship between environmental spatial heterogeneity and COVID-19 risk as entailed by the infection of CIR with COVID-19, which would make it possible to better plan managerial policies for public health.

scholarly journals Spatial Distribution of BTEX and Inorganic Pollutants During Summer Season in Yalova, Turkey

2017 ◽  
Vol 24 (4) ◽  
pp. 565-581
Author(s):  
Lokman Hakan Tecer ◽  
Sermin Tagil ◽  
Osman Ulukaya ◽  
Merve Ficici
Keyword(s):  
Spatial Distribution ◽  
Rural Areas ◽  
Air Pollutants ◽  
Passive Sampling ◽  
Temporal Variations ◽  
Average Concentration ◽  
Geographical Information ◽  
Moran’S I ◽  
Moran's I ◽  
Industrial Sites

Abstract The objective of this research is to determine the atmospheric concentrations and spatial distribution of benzene (B), toluene (T), ethylbenzene (E) and xylenes (X) (BTEX) and inorganic air pollutants (O3, NO2 and SO2) in the Yalova atmosphere during summer 2015. In this study, a combination of passive sampling and Geographical Information System-based geo-statistics are used with spatial statistics of autocorrelation to characterise the spatial pattern of the quality of air based on concentrations of these pollutants in Yalova. The spatial temporal variations of pollutants in the air with five types of land-use, residence, rural, highway, side road and industrial areas were investigated at 40 stations in Yalova between 7th August 2015 and 26th August 2015 using passive sampling. An inverse distance weighting interpolation technique was used to estimate variables at an unmeasured location from observed values at nearby locations. The spatial autocorrelation of air pollutants in the city was investigated using the statistical methods of Moran’s I in addition to the Getis Ord Gi. During the summer, highway and industrial sites had higher levels of BTEX then rural areas. The average concentration of toluene was measured to be 5.83 μg/m3 and this is the highest pollutant concentration. Average concentrations of NO2, O3 and SO2 are 35.64, 84.23 and 3.95 μg/m3, respectively. According to the global results of Moran’s I; NO2 and BTEX had positive correlations on a global space at a significant rate. Moreover, the autocorrelation analysis on the local space demonstrated significant hot spots on industrial sites and along the main roads.

scholarly journals Spatial Analysis of Beaked Whale Foraging During Two 12 kHz Multibeam Echosounder Surveys

2021 ◽  
Vol 8 ◽  
Author(s):  
Hilary Kates Varghese ◽  
Kim Lowell ◽  
Jennifer Miksis-Olds ◽  
Nancy DiMarzio ◽  
David Moretti ◽  
...  
Keyword(s):  
Marine Mammals ◽  
Spatial Clustering ◽  
Hot Spot ◽  
Moran’S I ◽  
Moran's I ◽  
Foraging Effort ◽  
Multibeam Echosounder ◽  
Spot Area ◽  
Comparison Results ◽  
Beaked Whales

To add to the growing information about the effect of multibeam echosounder (MBES) operation on marine mammals, a study was conducted to assess the spatial foraging effort of Cuvier’s beaked whales during two MBES surveys conducted in January of 2017 and 2019 off of San Clemente Island, California. The MBES surveys took place on the Southern California Antisubmarine Warfare Range (SOAR), which contains an array of 89 hydrophones covering an area of approximately 1800 km2 over which foraging beaked whales were detected. A spatial autocorrelation analysis of foraging effort was conducted using the Moran’s I (global) and the Getis-Ord Gi∗ (local) statistics, to understand the animals’ spatial use of the entire SOAR, as well as smaller areas, respectively, within the SOAR Before, During, and After the two MBES surveys. In both years, the global Moran’s I statistic suggested significant spatial clustering of foraging events on the SOAR during all analysis periods (Before, During, and After). In addition, a Kruskal-Wallis (comparison) test of both years revealed that the number of foraging events across analysis periods were similar within a given year. In 2017, the local Getis-Ord Gi∗ analysis identified hot spots of foraging activity in the same general area of the SOAR during all analysis periods. This local result, in combination with the global and comparison results of 2017, suggest there was no obvious period-related change detected in foraging effort associated with the 2017 MBES survey at the resolution measurable with the hydrophone array. In 2019, the foraging hot spot area shifted from the southernmost corner of the SOAR Before, to the center During, and was split between the two locations After the MBES survey. Due to the pattern of period-related spatial change identified in 2019, and the lack of change detected in 2017, it was unclear whether the change detected in 2019 was a result of MBES activity or some other environmental factor. Nonetheless, the results strongly suggest that the level of detected foraging during either MBES survey did not change, and most of the foraging effort remained in the historically well-utilized foraging locations of Cuvier’s beaked whales on the SOAR.

scholarly journals Spatial Interaction Effect of Population Density Patterns in Sub-Districts of Northeastern Thailand

2020 ◽  
Vol 9 (9) ◽  
pp. 556 ◽  
Author(s):  
Savittri Ratanopad Suwanlee ◽  
Jaturong Som-ard
Keyword(s):  
Population Density ◽  
Hot Spot ◽  
Spatial Interaction ◽  
Demographic Data ◽  
Moran’S I ◽  
Eastern Region ◽  
Strong Positive Correlation ◽  
Moran's I ◽  
The North ◽  
North Eastern

The north-eastern region in Thailand is the largest in area and population. Its average income per capita is, however, the lowest in Thailand. This phenomenon leads to migration to big cities, which are considered economic centres. We investigated the effect of spatial interaction on the population density pattern in 20 provinces in north-eastern Thailand. Data was obtained from the compilation and preparation of the demographic data of 2676 sub-districts for 2002–2017. A field survey was conducted through GPS at educational institutions, hospitals, airports, government offices, and shopping malls. The data was analysed using spatial autocorrelation analysis by a global indicator (global Moran’s I) and a local indicator (local Moran’s I and Getis–Ord Gi*). Eight Mueang districts exhibited the high-high (H-H) cluster pattern or hot spot at an increasing yearly rate. In addition, the area with the highest gravity was located near service sources and was found to have the largest population. Moreover, gravity interaction with service sources had a strong positive correlation with migration patterns. Thus, the cluster of areas with the greatest population density is located within the Mueang district in one of the provinces with most service sources, as these places attract people and consequently industrial factories and service trades.

scholarly journals Modelling spatial patterns and temporal trends of wildfires in Galicia (NW Spain)

2015 ◽  
Vol 24 (2) ◽  
pp. e022 ◽  
Author(s):  
Maria L. Loureiro ◽  
Jesús Barreal
Keyword(s):  
Rural Areas ◽  
Spatial Dependence ◽  
Urban Areas ◽  
Fixed Effects ◽  
Random Effect ◽  
Temporal Trends ◽  
Relative Size ◽  
Moran’S I ◽  
Contributing Factors ◽  
Moran's I

<p><em>Aim of study:</em> The goal of this paper is to analyse the importance of the main contributing factors to the occurrence of wildfires. <strong></strong></p><p><em>Area of study:</em> We employ data from the region of Galicia during 2001-2010; although the similarities shared between this area and other rural areas may allow extrapolation of the present results.</p><p><em>Material and Methods:</em> The spatial dependence is analysed by using the Moran’s I and LISA statistics. We also conduct an econometric analysis modelling both, the number of fires and the relative size of afflicted woodland area as dependent variables, which depend on the climatic, land cover variables, and socio-economic characteristics of the affected areas. Fixed effects and random effect models are estimated in order to control for the heterogeneity between the Forest Districts in Galicia.</p><p><em>Main results</em>: Moran’s I and LISA statistics show that there is spatial dependence in the occurrence of Galician wildfires. Econometrics models show that climatology, socioeconomic variables, and temporal trends are also important to study both, the number of wildfires and the burned-forest ratio.</p><p><em>Research highlights:</em> We conclude that in addition to direct forest actions, other agricultural or social public plans, can help to reduce wildfires in rural areas or wildland-urban areas. Based on these conclusions, a number of guidelines are provided that may foster the development of better forest management policies in order to reduce the occurrence of wildfires.</p><p><strong>Keywords:</strong> Cause-effect relationship; climatology; spatial and temporal indicators; fixed effects; random effects; socio-economic factors.</p>

Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
Matheus Supriyanto Rumetna ◽  
Eko Sediyono ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Spatial Autocorrelation ◽  
Land Use Changes ◽  
Geographical Information ◽  
Moran’S I ◽  
Complete Spatial Randomness ◽  
Moran's I ◽  
Global Moran’S I ◽  
Spatial Randomness

Abstract. Bantul Regency is a part of Yogyakarta Special Province Province which experienced land use changes. This research aims to assess the changes of shape and level of land use, to analyze the pattern of land use changes, and to find the appropriateness of RTRW land use in Bantul District in 2011-2015. Analytical methods are employed including Geoprocessing techniques and analysis of patterns of distribution of land use changes with Spatial Autocorrelation (Global Moran's I). The results of this study of land use in 2011, there are thirty one classifications, while in 2015 there are thirty four classifications. The pattern of distribution of land use change shows that land use change in 2011-2015 has a Complete Spatial Randomness pattern. Land use suitability with the direction of area function at RTRW is 24030,406 Ha (46,995406%) and incompatibility of 27103,115 Ha or equal to 53,004593% of the total area of Bantul Regency.Keywords: Geographical Information System, Land Use, Geoprocessing, Global Moran's I, Bantul Regency. Abstrak. Analisis Perubahan Tata Guna Lahan di Kabupaten Bantul Menggunakan Metode Global Moran’s I. Kabupaten Bantul merupakan bagian dari Provinsi Daerah Istimewa Yogyakarta yang mengalami perubahan tata guna lahan. Penelitian ini bertujuan untuk mengkaji perubahan bentuk dan luas penggunaan lahan, menganalisis pola sebaran perubahan tata guna lahan, serta kesesuaian tata guna lahan terhadap RTRW yang terjadi di Kabupaten Bantul pada tahun 2011-2015. Metode analisis yang digunakan antara lain teknik Geoprocessing serta analisis pola sebaran perubahan tata guna lahan dengan Spatial Autocorrelation (Global Moran’s I). Hasil dari penelitian ini adalah penggunaan tanah pada tahun 2011, terdapat tiga puluh satu klasifikasi, sedangkan pada tahun 2015 terdapat tiga puluh empat klasifikasi. Pola sebaran perubahan tata guna lahan menunjukkan bahwa perubahan tata guna lahan tahun 2011-2015 memiliki pola Complete Spatial Randomness. Kesesuaian tata guna lahan dengan arahan fungsi kawasan pada RTRW adalah seluas 24030,406 Ha atau mencapai 46,995406 % dan ketidaksesuaian seluas 27103,115 Ha atau sebesar 53,004593 % dari total luas wilayah Kabupaten Bantul. Kata Kunci: Sistem Informasi Georafis, tata guna lahan, Geoprocessing, Global Moran’s I, Kabupaten Bantul.

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature of urban areas. This study explored issue of measuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% of neighbourhoods' area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined. 

Spatial Autocorrelation in the Study of Neighbourhoods towards Smart Cities: Empirical Evidence from Kerman, Iran

2012 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Asra Hosseini
Keyword(s):  
Spatial Autocorrelation ◽  
Smart Cities ◽  
Moran’S I ◽  
Moran's I ◽  
Urban Planner ◽  
Universal Feature ◽  
Autocorrelation Method ◽  
Selection Of ◽  
Monotonic Relation

From earliest cities to the present, spatial division into residential zones and neighbourhoods is the universal feature ofurban areas. This study explored issue ofmeasuring neighbourhoods through spatial autocorrelation method based on Moran's I index in respect of achieving to best neighbourhoods' model for forming cities smarter. The research carried out by selection of 35 neighbourhoods only within central part of traditional city of Kerman in Iran. The results illustrate, 75% ofneighbourhoods, area in the inner city of Kerman had clustered pattern, and it shows reduction in Moran's index is associated with disproportional distribution of density and increasing in Moran's I and Z-score have monotonic relation with more dense areas and clustered pattern. It may be more efficient for urban planner to focus on spatial autocorrelation to foster neighbourhood cohesion rather than emphasis on suburban area. It is recommended characteristics of historic neighbourhoods can be successfully linked to redevelopment plans toward making city smarter, and also people's quality of life can be related to the way that neighbourhoods' patterns are defined.

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