Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.

This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8^th, 2020):

Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24^th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.

Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24^th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.

Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.

These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].

The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.

This tutorial focuses on some of the tools you can access in ArcGIS Online that cover proximity and hot spot analysis. This resource is part of the Career Path Series - GIS for Crime Analysis Lesson.Find other resources at k12.esri.ca/resourcefinder.

Stamp Out COVID-19An apple a day keeps the doctor away.Linda Angulo LopezDecember 3, 2020https://theconversation.com/coronavirus-where-do-new-viruses-come-from-136105SNAP Participation Rates, was explored and analysed on ArcGIS Pro, the results of which can help decision makers set up further SNAP-D initiatives.In the USA foods are stored in every State and U.S. territory and may be used by state agencies or local disaster relief organizations to provide food to shelters or people who are in need.US Food Stamp Program has been ExtendedThe Supplemental Nutrition Assistance Program, SNAP, is a State Organized Food Stamp Program in the USA and was put in place to help individuals and families during this exceptional time. State agencies may request to operate a Disaster Supplemental Nutrition Assistance Program (D-SNAP) .D-SNAP Interactive DashboardAlmost all States have set up Food Relief Programs, in response to COVID-19.Scroll Down to Learn more about the SNAP Participation Analysis & ResultsSNAP Participation AnalysisInitial results of yearly participation rates to geography show statistically significant trends, to get acquainted with the results, explore the following 3D Time Cube Map:Visualize A Space Time Cube in 3Dhttps://arcg.is/1q8LLPnetCDF ResultsWORKFLOW: a space-time cube was generated as a netCDF structure with the ArcGIS Pro Space-Time Mining Tool : Create a Space Time Cube from Defined Locations, other tools were then used to incorporate the spatial and temporal aspects of the SNAP County Participation Rate Feature to reveal and render statistically significant trends about Nutrition Assistance in the USA.Hot Spot Analysis Explore the results in 2D or 3D.2D Hot Spotshttps://arcg.is/1Pu5WH02D Hot Spot ResultsWORKFLOW: Hot Spot Analysis, with the Hot Spot Analysis Tool shows that there are various trends across the USA for instance the Southeastern States have a mixture of consecutive, intensifying, and oscillating hot spots.3D Hot Spotshttps://arcg.is/1b41T43D Hot Spot ResultsThese trends over time are expanded in the above 3D Map, by inspecting the stacked columns you can see the trends over time which give result to the overall Hot Spot Results.Not all counties have significant trends, symbolized as Never Significant in the Space Time Cubes.Space-Time Pattern Mining AnalysisThe North-central areas of the USA, have mostly diminishing cold spots.2D Space-Time Mininghttps://arcg.is/1PKPj02D Space Time Mining ResultsWORKFLOW: Analysis, with the Emerging Hot Spot Analysis Tool shows that there are various trends across the USA for instance the South-Eastern States have a mixture of consecutive, intensifying, and oscillating hot spots.Results ShowThe USA has counties with persistent malnourished populations, they depend on Food Aide.3D Space-Time Mininghttps://arcg.is/01fTWf3D Space Time Mining ResultsIn addition to obvious planning for consistent Hot-Hot Spot Areas, areas oscillating Hot-Cold and/or Cold-Hot Spots can be identified for further analysis to mitigate the upward trend in food insecurity in the USA, since 2009 which has become even worse since the outbreak of the COVID-19 pandemic.After Notes:(i) The Johns Hopkins University has an Interactive Dashboard of the Evolution of the COVID-19 Pandemic.Coronavirus COVID-19 (2019-nCoV)(ii) Since March 2020 in a Response to COVID-19, SNAP has had to extend its benefits to help people in need. The Food Relief is coordinated within States and by local and voluntary organizations to provide nutrition assistance to those most affected by a disaster or emergency.Visit SNAPs Interactive DashboardFood Relief has been extended, reach out to your state SNAP office, if you are in need.(iii) Follow these Steps to build an ArcGIS Pro StoryMap:Step 1: [Get Data][Open An ArcGIS Pro Project][Run a Hot Spot Analysis][Review analysis parameters][Interpret the results][Run an Outlier Analysis][Interpret the results]Step 2: [Open the Space-Time Pattern Mining 2 Map][Create a space-time cube][Visualize a space-time cube in 2D][Visualize a space-time cube in 3D][Run a Local Outlier Analysis][Visualize a Local Outlier Analysis in 3DStep 3: [Communicate Analysis][Identify your Audience & Takeaways][Create an Outline][Find Images][Prepare Maps & Scenes][Create a New Story][Add Story Elements][Add Maps & Scenes] [Review the Story][Publish & Share]A submission for the Esri MOOCSpatial Data Science: The New Frontier in AnalyticsLinda Angulo LopezLauren Bennett . Shannon Kalisky . Flora Vale . Alberto Nieto . Atma Mani . Kevin Johnston . Orhun Aydin . Ankita Bakshi . Vinay Viswambharan . Jennifer Bell & Nick Giner

This map was created using Hot Spot Analysis. Hot Spot Analysis was performed on four layers: sexual assault crimes, white population, black population, and Asian population. All layers were normalized by area.

Additional file 1: Table A1. List of California Medical Service Study Areas (MSSA) that have high-high rates of CA-MRSA clustering (HH), low-low rates of CA-MRSA clustering (LL), and low-high rates of CA-MRSA clustering (LH), 2016-2019. Table A2. Ten California MSSAs with the highest and lowest risk ratio for CA-MRSA between 2016-2019. A3. Analysis code - R-Markdown html file.

IntroductionChildhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey.MethodThe current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association.ResultThe prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran’s I = 0.40, p40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6–23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting.ConclusionIn Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

The Regional Crime Analysis GIS (RCAGIS) is an Environmental Systems Research Institute (ESRI) MapObjects-based system that was developed by the United States Department of Justice Criminal Division Geographic Information Systems (GIS) Staff, in conjunction with the Baltimore County Police Department and the Regional Crime Analysis System (RCAS) group, to facilitate the analysis of crime on a regional basis. The RCAGIS system was designed specifically to assist in the analysis of crime incident data across jurisdictional boundaries. Features of the system include: (1) three modes, each designed for a specific level of analysis (simple queries, crime analysis, or reports), (2) wizard-driven (guided) incident database queries, (3) graphical tools for the creation, saving, and printing of map layout files, (4) an interface with CrimeStat spatial statistics software developed by Ned Levine and Associates for advanced analysis tools such as hot spot surfaces and ellipses, (5) tools for graphically viewing and analyzing historical crime trends in specific areas, and (6) linkage tools for drawing connections between vehicle theft and recovery locations, incident locations and suspects' homes, and between attributes in any two loaded shapefiles. RCAGIS also supports digital imagery, such as orthophotos and other raster data sources, and geographic source data in multiple projections. RCAGIS can be configured to support multiple incident database backends and varying database schemas using a field mapping utility.

Feature layer created by running the Find Hot Spots tool in the ArcGIS API for Python on the Canadian Proximity Measures data for downtown Toronto (see https://edu.maps.arcgis.com/home/item.html?id=f0a4f870eeb5499a8df8ae47281fb028). In this layer, hot and cold spots were found for the prox_idx_health field (proximity to healthcare facilities).---Adapted from Statistics Canada, Proximity Measures Database, 2020, and Boundary Files, 2016 Census, Statistics Canada Catalogue no. 92-160-X. This does not constitute an endorsement by Statistics Canada of this product.

Evaluation of 20 network scores based on protein residue contact maps constructed from 3 coevolution analysis tools (DeepMetaPSICOV, RaptorX, and SPOT-Contact) and AlphaFold-predicted structures.

This project has two aims, each one described below.Aim 1: To quantify rural-urban disparities in rehabilitation therapy utilization (IRFs/ SNFs/ HHAs) among FFS Medicare beneficiaries (2021) and evaluate their change over time (2013-2021). Hypothesis 1: Rural Fee For Service (FFS) Medicare beneficiaries had lower therapy utilization than urban counterparts in 2021. Hypothesis 2: Rural disparities have been stagnant or increased as opposed to significantly reduced over time (2013-2021), stratified for the pre- (2013-2019) and post-pandemic (2020-2021) times. We will use hierarchical linear multiple regressions. Dependent variable: counties’ therapy utilization rate for IRFs, SNFs, and HHAs combined. Independent variable: rural area, per two indicators: a) rural residency of FFS beneficiaries b) rural county gradient. Covariates: 1) FFS beneficiaries’ characteristics; 2) FFS Medicare expenditures; 3) counties’ disability statistics, e.g., poverty; 4) community-level health, health access and social determinants of health; and 6) regions and states. The significance of the interaction between years and rurality will be tested. Aim 2: To build interactive, user-centered maps of rehabilitation therapy utilization (IRFs / SNFs / HHAs), identifying hot spots of low utilization (in 2021) and their evolving trends (2013-2021). A GIS (ArcGIS Pro) will be used to spatiotemporally analyze the data used in the Aim 1. First, we will develop choropleth maps: gradients of county-level utilization rates, intersected with rural areas. Second, hot spot analyses (statistical spatial clustering) will map clusters of counties with low utilization. Third, a spatiotemporal, emerging hot spot analysis (2013-2021) will map areas with up to eight types of time-trends such as those showing an intensifying or persistent low utilization. The spatiotemporal analysis will be also stratified for the pre- (2013-2019) and post-pandemic (2020-2021) time periods. All the maps, with customizable options, will be shared online for public access. An Advisory Group of target end-users (e.g., disability advocates, public health agents) will provide input throughout to design the maps’ attributes and refine them after beta testing

Structural characterization of small molecule binding site hotspots within the global proteome is uniquely enabled by photoaffinity labeling (PAL) coupled with chemical enrichment and unbiased analysis by mass spectrometry (MS). MS-based binding site maps provide structural resolution of interaction sites in conjunction with identification of target proteins. However, binding site hotspot mapping has been confined to relatively simple small molecules to date; extension to more complex compounds would enable the structural definition of new binding modes in the proteome. Here, we extend PAL and MS methods to derive a binding site hotspot map for the immunosuppressant rapamycin, a complex macrocyclic natural product that forms a ternary complex with the proteins FKBP12 and FRB. Photo-rapamycin was developed as a diazirine-based PAL probe for rapamycin, and the FKBP12–photo-rapamycin–FRB ternary complex formed readily in vitro. Photoirradiation, digestion, and MS analysis of the ternary complex revealed a McLafferty rearrangement product of photo-rapamycin conjugated to specific surfaces on FKBP12 and FRB. Molecular modeling based on the binding site map revealed two distinct conformations of complex-bound photo-rapamycin, providing a 5.0 Å distance constraint between the conjugated residues and the diazirine carbon and a 9.0 Å labeling radius for the diazirine upon photoactivation. These measurements may be broadly useful in the interpretation of binding site measurements from PAL. Thus, in characterizing the ternary complex of photo-rapamycin by MS, we applied binding site hotspot mapping to a macrocyclic natural product and extracted precise structural measurements for interpretation of PAL products that may enable the discovery of new binding sites in the “undruggable” proteome.

IntroductionChildhood stunting is a global public health concern, associated with both short and long-term consequences, including high child morbidity and mortality, poor development and learning capacity, increased vulnerability for infectious and non-infectious disease. The prevalence of stunting varies significantly throughout Ethiopian regions. Therefore, this study aimed to assess the geographical variation in predictors of stunting among children under the age of five in Ethiopia using 2019 Ethiopian Demographic and Health Survey.MethodThe current analysis was based on data from the 2019 mini Ethiopian Demographic and Health Survey (EDHS). A total of 5,490 children under the age of five were included in the weighted sample. Descriptive and inferential analysis was done using STATA 17. For the spatial analysis, ArcGIS 10.7 were used. Spatial regression was used to identify the variables associated with stunting hotspots, and adjusted R2 and Corrected Akaike Information Criteria (AICc) were used to compare the models. As the prevalence of stunting was over 10%, a multilevel robust Poisson regression was conducted. In the bivariable analysis, variables having a p-value < 0.2 were considered for the multivariable analysis. In the multivariable multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval is presented to show the statistical significance and strength of the association.ResultThe prevalence of stunting was 33.58% (95%CI: 32.34%, 34.84%) with a clustered geographic pattern (Moran’s I = 0.40, p40 (APR = 0.74, 95%CI: 0.55, 0.99). Children whose mother had secondary (APR = 0.74, 95%CI: 0.60, 0.91) and higher (APR = 0.61, 95%CI: 0.44, 0.84) educational status, household wealth status (APR = 0.87, 95%CI: 0.76, 0.99), child aged 6–23 months (APR = 1.87, 95%CI: 1.53, 2.28) were all significantly associated with stunting.ConclusionIn Ethiopia, under-five children suffering from stunting have been found to exhibit a spatially clustered pattern. Maternal education, wealth index, birth interval and child age were determining factors of spatial variation of stunting. As a result, a detailed map of stunting hotspots and determinants among children under the age of five aid program planners and decision-makers in designing targeted public health measures.

Model comparison of OLS, GWR and MGWR model.

Created using ArcGIS Pro Geoprocessing tools (Create Space Time Cube, Emerging Hot Spot Analysis, and Enrich Layer) and the ArcGIS R Bridge. The EBest function, part of the spdep package was used to calculate an Empirical Bayes smoothed crime rate with 2016 population estimates. This procedure is presented as part of the R-ArcGIS Workflow Demo on GeoNet.Relative Burglary Risk is the natural log (Ln) of the kernel density of burglaries g(x) divided by the kernel density of households g(y) calculated using CrimeStat. Note: Ten months of burglary data (the minimum required) were used for this initial analysis. Also Note: These locations are one-half kilometer square polygons. It will be updated in the future as more data from the Albuquerque Police Department is obtained (see ABQ Data).Please see the web map for another similar way to present these results.More information at (http://www.unm.edu/~lspear/other_nm.html).

Objectives: To map the alcohol hot spots and understand the Sociodemographic Indices (SDI) affecting alcohol consumption in Indian men and women.Methods: Data from National Family Health Survey-4 carried out from 2015 to 2016 with a sample size of 103,411 men and 699,686 women were used for Geographic Information System mapping, and hot spot identification by spatial statistics (Getis-Ord Gi*). Bivariate analyses and multiple logistic regressions were used to analyze SDI.Results: India has three major alcohol hot spots: (1) North-East (NE) states, (2) Eastern Peninsular states formed by Chhattisgarh, Odisha, Jharkhand, and Telangana, and (3) Southern states of Tamil Nadu and Kerala. Hot spot analysis strongly correlated with region-wise analysis of SDI. Respondents who consumed tobacco have higher odds (men adjusted odds ratio [aOR]: 5.42; women aOR: 4.30) of consuming alcohol. Except for religion and social category, other socioeconomic factors have a low to moderate effect on alcohol consumption.Conclusions: Hot spots and high-risk districts of alcohol consumption identified in this study can guide public health policies for targeted intervention. Alcohol use is at the discretion of individual states and union territories, and stringent anti-alcohol policies strictly enforced across India are the keys to control alcohol use.

Comparison of multi- level model for childhood febrile illness Ethiopia 2016.

The ordinary least square regression analysis result for childhood febrile illness Ethiopia 2016.

Bi variable and multi variable multilevel log-binomial regression for childhood febrile illness Ethiopia 2016(final model).

BackgroundTumor-infiltrating lymphocytes (TILs), essential for the anti-tumor response, are now recognized as promising and cost-effective biomarkers with both prognostic and predictive value. They are crucial in the precision treatment of breast cancer, particularly for predicting clinical outcomes and identifying candidates for immunotherapy. This study aims to encapsulate the current knowledge of TILs in breast cancer research while evaluating research trends both qualitatively and quantitatively.MethodsPublications on TILs in breast cancer studies from January 1, 2004, to December 31, 2023, were extracted from the Web of Science Core Collection. Co-occurrence and collaboration analyses among countries/regions, institutions, authors, and keywords were performed with Bibliometrix R packages and VOSviewer software. CiteSpace was used for reference and keyword burst detection, while high-frequency keyword layouts were generated using BICOMB. gCLUTO was employed for biclustering analysis of the binary co-keyword matrix.ResultsA total of 2,066 articles on TILs in breast cancer were identified. Between 2004 and 2023, the USA and Milan University led productivity in terms of country/region and institution, respectively. The journals “CANCERS,” “Breast Cancer Research and Treatment,” and “Frontiers in Oncology” published the most articles on this topic. Loi S was the leading author, with the highest number of publications and co-citations. Co-keyword analysis revealed six research hotspots related to TILs in breast cancer. The pathological assessment of TILs using artificial intelligence (AI) remains in its early stages but is a key focus. Burst detection of keywords indicated significant activity in “immune cell infiltration”, “immune checkpoint inhibitors”, and “hormone receptor” over the past three years.ConclusionThis study reviews recent advancements and trends in TILs research in breast cancer using scientometric analysis. The findings offer valuable insights for funding decisions and developing innovative strategies in TILs research, highlighting current research frontiers and trends.

This dataset represents the results of a project that compiled available range information for three taxonomic groups representing 211 species (159 birds, 45 mammals, and 5 amphibians) identified as Species of Greatest Conservation Need (SGCN) by the 2015 Alaska Wildlife Action Plan (SWAP) Appendix A (https://www.adfg.alaska.gov/index.cfm?adfg=wildlifediversity.swap) in addition to 2 amphibian species native to Alaska.

The goal of this effort was to create an initial set of statewide heatmaps of SGCN richness. Files include: (1) a set of 21 species richness heat maps depicting the sum of overlapping range maps from multiple SGCNs; (2) shapefiles of species range maps for Alaska’s terrestrial SGCN, with all species ranked (high, moderately high, moderate, low) in terms of relative conservation and management priority based on the Alaska Species Ranking System (ASRS; https://accs.uaa.alaska.edu/wildlife/alaska-species-ranking-system); (3) shapefiles of species in decline for birds and marine mammals (as listed in SWAP Appendix A); and (4) a file that cross-walks each SGCN by species code, common name, and scientific name.

Complete information describing how environmental variables correlated with species richness is provided in the final report (http://data.snap.uaf.edu/data/Base/Other/Species/State_Wildlife_Grant_Final_Report_20Sept24.pdf). Species richness maps were derived from species-specific, 6th-level hydrologic unit (HUC12) occupancy maps developed by the Alaska Gap Analysis Project (https://accscatalog.uaa.alaska.edu/dataset/alaska-gap-analysis-project). Hotspot maps highlight all HUCs containing more than 60% of considered amphibian species or 80% of the maximum number of co-occurring bird or mammal species. Species richness values were derived by summing the number of species with overlapping ranges. A gradient boosting machine algorithm quantified relationships between SGCN hotspots and a set of 24 climatic, topographic, and habitat predictors.

It is important to note that species ranges are modeled and extrapolated from limited data. They may be affected by changes in our understanding of species' ranges, changes in taxonomy, and changes in what we consider to be the best tools and data for creating distribution models using presence-only data, and may overestimate actual ranges. These datasets and any associated maps and other products are intended to provide a landscape-level overview only. It is highly recommended that any use of these datasets be undertaken in conjunction with expert advice from the Alaska Department of Fish and Game (see contact information below).