This course will introduce you to two of these tools: the Hot Spot Analysis (Getis-Ord Gi*) tool and the Cluster and Outlier Analysis (Anselin Local Moran's I) tool. These tools provide you with more control over your analysis. You can also use these tools to refine your analysis so that it better meets your needs.GoalsAnalyze data using the Hot Spot Analysis (Getis-Ord Gi*) tool.Analyze data using the Cluster and Outlier Analysis (Anselin Local Moran's I) tool.

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 GIS protocol is primarily intended as supplementary material to the article (Štular et al., 2022). The article contains important contextual information about its intended use. In short, this GIS protocol was developed for the purposes of archaeological regional analysis of spatial data. The data are provided elsewhere in spreadsheet format (Štular et al., 2021). Data in GIS format are included in this repository. The GIS protocol can be used with any relevant data for any purpose as long as the data format matches the format of the included data.

Includes GIS protocol (textual description) and GIS data in *.shp format.

The following report outlines the workflow used to optimize your Find Hot Spots result:There were 866 valid input features.ASIAN Properties:Min0.0000Max4478.0000Mean145.5242Std. Dev.381.7524There were 10 outlier locations; these were not used to compute the optimal fixed distance band.Scale of AnalysisThe optimal fixed distance band selected was based on peak clustering found at 5344.2049 Meters.Hot Spot AnalysisThere are 596 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high ASIAN values cluster.Blue output features represent cold spots where low ASIAN values cluster.

Although miniaturized data loggers allow new insights into avian migration, incomplete knowledge of basic patterns persists, especially for nightjars. Using GPS data loggers, this study examined migration ecology of the Eastern whip-poor-will (Antrostomus vociferus), across three migration strategies: flyover, short-stay, and long-stay. We documented migration movements, conducted hotspot analyses, quantified land cover within 1-km and 5-km buffers at used and available locations, and modeled habitat selection during migration. From 2018-2020 we captured breeding whip-poor-wills from three study sites in Massachusetts and programmed GPS tags to collect data during fall and spring migration periods. Across 19 individual males (nine of them with repeated years of data), GPS tags collected 479 locations, where 30% were classified as flyover points, 33% as short-stays, and 37% as long-stay locations. We documented seasonal flexibility in migration duration, routes, and stopover locations among individuals and between years. Analyses identified hotspot clusters in fall and spring migration in the Sierra de Tamaulipas in Mexico. Land cover at used locations differed across location types at the 5-km scale, where closed forest cover increased and crop cover decreased for flyover, short-stay, and long-stay locations, and urban cover was lowest at long-stay locations. Discrete choice modeling indicated that habitat selection by migrating whip-poor-wills differs depending on the scale and migration strategy. For example, at the 5-km scale birds avoided urban cover at long-stay locations and selected closed forest cover at short-stay locations. We suggest that whip-poor-wills may use land cover cues at large spatial scales, like 5-km, to influence rush or stay tactics during migration. Methods From 2018-2020, we captured breeding whip-poor-wills from three study sites in Massachusetts and programmed GPS tags to collect data during fall and spring migration periods. Across 19 individual males (nine of them with repeated years of data), GPS tags collected 479 locations, where 30% were classified as flyover points, 33% as short-stays, and 37% as long-stay locations. Data processing We filtered and retained migration data points when loggers connected to ≥ 4 satellites and points had dilution of precision values < 5 to ensure a 3D fix of the location (Forrest et al. 2022, Bakermans et al. 2022). Using 30-m USGS DEM (digital elevation model; http://ned.usgs.gov) data, we generated the altitude of each point by converting the GPS tags’ altitude to altitude above sea level and then subtracted the local elevation (from the DEM) from the bird’s altitude (A. Korpach, pers. communication). Next, we classified migration points based on altitude and number of points at a single location as either flyover, short-stay, or long-stay. Long-stays were locations with ≥ 2 GPS points within the same vicinity (i.e., < 10 km). Short-stay and flyovers consisted of one GPS point at a single location. We differentiated short-stay versus flyover points by altitude based on the altitudes of birds at long-stay locations (mean = 17 m, range = 121 m). Short-stays were locations with elevations < 100 m (mean = 15 m), and flyover locations had an altitude ≥ 100 m above the ground (mean = 800 m). Hotspot Analyses To identify areas of high or low use during migration, we ran an optimized hotspot analysis in ArcGIS 10.8.2 to identify statistically significant spatial clusters of high (hotspot) and low values (coldspot) of migration locations using the Getis-Ord Gi* statistic (Sussman et al. 2019). This tool can “aggregate data, identify an appropriate scale of analysis, and correct for both multiple testing and spatial dependence” (ESRI 2021). Land cover classification We used ArcGIS and quantified land cover types from 2019 data using the 100-m Copernicus Global Land Service layer (Buchhorn et al. 2020). Land cover types were classified as (a) closed forest, (b) open forest, (c) shrubland, (d) herbaceous vegetation (hereafter, grassland), (e) herbaceous wetland, (f) cropland, (g) bare, (h) fresh- or saltwater, and (i) developed land (Buchhorn et al. 2020). Using the geoprocessing features of ArcMap, we quantified land cover at 5-km and 1-km circle at an actual migration location (i.e., used) and random locations (i.e., available). Habitat selection We used discrete choice modeling to determine habitat selection of Eastern whip-poor-will during migration. Discrete choice models examine the probability that an individual chooses a location based on a choice set of alternative available locations (Cooper and Millspaugh 1999). Choice sets included one used location based on the GPS fix and ten available locations. We constructed separate models for each type of migration point (i.e., flyover, short-stay, and long-stay) and spatial scale (i.e., 1 km and 5 km) with individual as a random effect. We used package jagsUI (Kellner 2021) with the software JAGS 4.3.1 (Plummer 2003).

OverviewDue to the increasing size and complexity of global forest monitoring data sources, analysis and interpretation tools for this data are ever more important for intervention efforts, allowing for the quick identification and interpretation of significant forest loss. The emerging hot spots data set identifies the most significant clusters of primary forest loss between 2002-2023 at a country level basis, on a tropical scale. The term ‘hot spot’ is defined as an area that exhibits statistically significant clustering in the spatial patterns of loss. In this analysis, observed patterns of primary forest loss are likely to be attributable to underlying, as opposed to random, spatial processes. The different categories of hot spots are described below:New: A location that is a statistically significant hot spot only for the year 2023 and has never been a hot spot before.Sporadic: A location that is an on-again then off-again hot spot. Less than 20 of the 22 years have been statistically significant hot spots.Intensifying: A location that has been a statistically significant hot spot for more than 19 of the 22 years (>90%), including the most recent year (2023). In addition, the intensity of clustering of high counts in each year is increasing.Persistent: A location that has been a statistically significant hot spot for more than 19 of the 22 years (>90%), with no discernible trend indicating an increase or decrease in the intensity of clustering over time.Diminishing: A location that has been a statistically significant hot spot for more than 19 of the 22 years (>90%). In addition, the intensity of clustering of high counts in each year is decreasing, or the most recent year (2023) is not hot.The emerging hot spots analysis uses the annual Hansen et al 2013 tree cover loss data set between the years 2002 – 2023, the Turubanova et al. 2018 primary forest extent data set for the year 2001, and the ESRI ArcGIS Emerging Hot Spot Analysis geoprocessing tool. In this analysis, primary forest is defined as mature natural humid tropical forest cover that has not been completely cleared and regrown in recent history. Forest loss is defined as ‘stand replacement disturbance,’ or the complete removal of tree cover canopy at the Landsat pixel scale. The emerging hot spots analysis tool uses a combination two statistical measures, the Getis-Ord Gi* statistic to identify the location and degree of spatial clustering of forest loss, and the Mann-Kendall trend test to evaluate the temporal trend over time.The forest loss data used in this analysis has a user’s accuracy of 87% and a producer’s accuracy of 83.1% across the tropical biome. Additionally, because this analysis was run for individual countries, results are relative to the patterns and amount of loss in each country. Results should not be directly compared between countries - please use caution when viewing layer at a global scale.Geographic Coverage: TropicsFrequency of Updates: AnnualDate of Content: 2002-2023

Data was used for the study "Unveiling Leptospirosis Hotspots with Earth Observation and AI".The study embarks on the spatiotemporal analysis of leptospirosis hotspot areas in Selangor using secondary data from 2011 to 2019. Point shape files were plotted based on the coordinates of case's possible source of infection. Cases were aggregated according to respective subdistrict polygon areas. Monthly Hotspot analysis was initially conducted using the Getis Ord Gi* in ArcGIS Pro software. Satellite data for monthly rainfall and LST was retrieved from the NASA Geovanni EarthData website. Monthly values (2-11-2019) for every subdistrict were extracted using ArcGIS Pro software.Data contains monthly data for 55 subdistricts in Selangor (not individually labelled) from 2011 to 2019 - (5 columns and 5940 rows)leptospirosis hotspot (H) (Yes[1] or No[0].Precipitation (P) - monthly values in millimetresLand Surface Temperature (T) - monthly values in degrees Celsius (oC)The code snippets used for machine learning data analysis are also available. Codes include three algorithms used:LGBM, 2. Random Forest, and 3. SVM

These files were generated from Extract Data. They use hot spot analysis to identify statistically significant clusters in populations of different demographics. Hot spot analysis for White, Black, and Asian populations are included, as well as the total population.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

Animal mortality on roads is one of the main concerns on wildlife conservation. Due to their habitat requirements, amphibians became one of the most commonly road-killed group and this may affect their population viability. Implementation of mitigation measures may overcome the problem. However, due to the extensive road network, their application is very expensive and required a better understanding in where they should be implemented. Mortality hotspots can be identified as clusters of road-killed records) using GIS (Geographic Information Systems). Although there are several statistical methods available, it is lacking a comparison analysis of them in order to understand their pros and contras. The aim of this study was to analyse possible differences between global, multi-scale and local spatial analysis methods in defining hotspots using amphibian road fatality data collected in northern Portugal country roads. We calculated the Nearest neighbor index, Morans I and Getis-ord General in order to compare the global clustering of points in seven sampled roads, and three were identified as clustered. We used Ripley K-function, Ripley L-function and F function to calculate the best scale for Malo's equation and Kernel density analysis in detecting hotspots and we compared their detection performance with Local Indicators of Association (LISA) (i.e Local Moran's I and Getis-ord Gi). Three different GIS software applications were used: ArcGis, Quantum GIS with R (opensource) and GeoDa (opensource). Results showed the importance of using multidistance spatial cluster analysis to define the best scale for hotspot detection with Malo´s equation and Kernel density analysis. Here we also suggest the advantages of Local Indicators of Association (LISA) for detecting clusters with the contribution of each individual observation (Local Morans I and Getis-ord Gi).

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.

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Short Description

Freshwater fish sampling sites ranked using a scoring system based on species diversity and abundance, displayed as a weighted score which takes nearby sampling sites into account. Statistical significance is dependent on a site with a high (or low) Biodiversity score being surrounded by other sites with high (or low) scores as well.

This dataset has been provided to the BA Programme for use within the programme only. Third parties may request a copy of the data from DPI Water (previously known as the NSW Office of Water). http://www.water.nsw.gov.au/.

Purpose

To identify regions of high fish biodiversity value in NSW rivers.

Dataset History

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Detailed Description

A biodiversity scoring system was developed that accounts for both species richness (the number of different native species present) and each species' abundance. The scoring system identifies sites which support the highest abundance of the largest number of native fish species. Sites are then scaled to a percentage of the highest ranked site within the reporting region.

Numbers of fish caught from the catch dataset collected by Fisheries NSW using electrofishing at all sites that have been sampled in NSW sampled between 1 January 2002 and 31 December 2011 (a 10 year period) were standardised to catch per minute of electrofishing to account for variation in sampling effort across sites. Estuarine fishes sampled within coastal freshwater habitats were included as being native fish. Prior to analysis, sites were partitioned into meaningful groupings in order to ensure like sites were being compared. For the NSW-wide scale analysis, sites were stratified using the freshwater fish bioregionalisation model of Growns and West (2008), with an additional bioregion for sites within the Lake Eyre Basin - Bulloo catchments in northwest NSW. For CMA scale analysis, sites were stratified into Lowland (3 - 400 m) and Upland (401 - 1,780 m) altitude bands within each CMA area, noting that sites within the Lake Eyre Basin drainage division were analysed separately from the Murray-Darling Basin portion of the Western CMA area.

To calculate the Biodiversity score, each site within each group was ranked for each species in ascending order. Hence, sites where a species was absent had a rank of 0 and the site that had the highest abundance for that species had the highest rank. The ranks for each species were then summed across each sampling site to provide a 'Sum of Ranks'. The site with the highest 'Sum of Ranks' was identified in each zone and then the Biodiversity Score of each site was expressed as a percentage of the 'Sum of Ranks' for the most highly ranked site in each zone.

A cluster analysis was then undertaken on the data for each bioregion or altitude band using the Getis-Ord Hot Spot Analysis tool in ArcGIS (Fischer and Getis 2010). The Getis-Ord statistic (Z score) identifies whether features with either particularly high values or particularly low values tend to cluster in a given area. This tool works by looking at each feature within the context of neighbouring features. If a feature's value is high, and the values for all of its neighbouring features is also high, it is considered a part of a 'biodiversity hot spot'. The local sum for a feature and its neighbours is compared proportionally to the sum of all features; if the local sum is considerably different from the expected local sum, that difference is deemed to be too large to be the result of random chance. When this occurs, a statistically significant Z score is generated.

Suggested display schema

Use 'Quantities/Graduated Colours' on GiZ Score field.

Number of Classes = 8

Classification Method = Geometric Interval (provides weighting to distal part of the distribution curve)

Use a colour ramp from green to red with yellow intermediate (select HSV colour algorithm and full brightness on both black and white sliders)

Symbol size = 9

Flip symbols (ie. so that low values are red and high values are green)

No 'normalisation'

Edit 'label' text to delete numeric values and identify the most negative range as "Low Biodiversity" and the most positive range as "High Biodiversity"

Extent

All NSW

Responsible Person

Dr Dean Gilligan

Senior Research Scientist

NSW Department of Primary Industries (Fisheries)

References

Fischer and Getis 2010: Handbook of Applied Spatial Analysis. Manfred M. Fischer and Arthur Getis. Baker & Taylor, January 2010

Growns and West 2008: Classification of aquatic bioregions through the use of distributional modelling of freshwater fish, : Ivor Growns, and Greg West. Ecological Modelling, Volume 217, Issues 1-2, 24 September 2008, Pages 79-86.

Dataset Citation

NSW Office of Water (2014) Freshwater Fish Biodiversity Hotspots. Bioregional Assessment Source Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/6093bd55-a978-4fcc-a8fa-6881fa015329.

BackgroundCesarean Section (CS) is the most popular surgery worldwide in obstetric care to save a mother’s or the fetus’s life. The prevalence of CS delivery in Ethiopia was 0.7% and 1.9% in 2000 and 2016 respectively and its spatial distribution and variation in Ethiopia are limited. This study provides evidence for healthcare providers and pregnant women on the national CS geospatial distribution and variation to promote evidence-based decision-making and improve maternal and neonatal outcomes. Therefore, this study aimed to determine geospatial patterns and individual and community-level factors of CS deliveries in Ethiopia.MethodA secondary data analysis of 5,527 weighted samples of mothers using the 2019 Ethiopian mini demographic and health survey was conducted. The spatial hotspot analysis using Getis-Ord Gi* hot spot analysis of ArcGIS version 10.7.1 was used to show the spatial cluster of CS and multilevel mixed effect logistic regression analyses were employed. Statistical significance was declared at p-value

ObjectivesThis study used (Geographic Information System) GIS technology to analyze the spatiotemporal distribution of influenza incidence in Qinghai from 2009 to 2023, based on influenza surveillance data.MethodsThis study first accessed the influenza data sets of Qinghai Province from 2009 to 2023 through the Chinese Infectious Disease Surveillance System. Subsequently, trend charts of influenza incidence in each city and prefecture were employed to illustrate the trends of influenza incidence during the period from 2009 to 2023. To explore the risks of influenza incidence in different counties and districts, methods including spatial autocorrelation, cluster analysis, hotspot analysis, Gravity center shift model, and standard deviation ellipse were utilized.ResultsThe study showed that the incidence of influenza showed significant fluctuations, with marked spikes in 2019 and 2023. Spatial autocorrelation analysis revealed significant positive autocorrelation in 2015, 2017–2019, and 2022–2023 (Moran’s I > 0 and p

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.

Police data from Kigali Traffic Police reports of road traffic crashes occurring from January 1 to December 31, 2013. Data includes categorization of types of road traffic crashes, injuries, and geospatial coordinates.

2016 Chicago sexual assault crimes and race hot spot analysis

The present dataset provides necessary indicators of the climate change vulnerability of Bangladesh in raster form. Geospatial databases have been created in Geographic Information System (GIS) environment mainly from two types of raw data; socioeconomic data from the Bangladesh Bureau of Statistics (BBS) and biophysical maps from various government and non-government agencies. Socioeconomic data have been transformed into a raster database through the Inverse Distance Weighted (IDW) interpolation method in GIS. On the other hand, biophysical maps have been directly recreated as GIS feature classes and eventually, the biophysical raster database has been produced. 30 socioeconomic indicators have been considered, which has been obtained from the Bangladesh Bureau of Statistics. All socioeconomic data were incorporated into the GIS database to generate maps. However, the units of some variables have been adopted directly from BBS, some have been normalized based on population, and some have been adopted as percentages. 12 biophysical system indicators have also been classified based on the collected information from different sources and literature. Biophysical maps are mainly classified in relative scales according to the intensity. These geospatial datasets have been analyzed to assess the spatial vulnerability of Bangladesh to climate change and extremes. The analysis has resulted in a climate change vulnerability map of Bangladesh with recognized hotspots, significant vulnerability factors, and adaptation measures to reduce the level of vulnerability.