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.
Feature layer created by running the Find Hot Spots tool in ArcGIS Online on the Canadian Proximity Measures data for the city of Toronto (see https://edu.maps.arcgis.com/home/item.html?id=fb599a53973843358d126e1404fe53ba -- filtered to CMANAME = 'Toronto'). 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.---The following report outlines the workflow used to optimize your Find Hot Spots result:There were 3702 valid input features.PROX_IDX_HEALTH Properties:Min0.0001Max0.8205Mean0.0482Std. Dev.0.0767There were 46 outlier locations; these will not be used to compute the optimal fixed distance band.Scale of AnalysisThe optimal fixed distance band was based on the average distance to 30 nearest neighbors: 1196.0000 Meters.Hot Spot AnalysisThere are 2289 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high PROX_IDX_HEALTH value cluster.Blue output features represent cold spots where low PROX_IDX_HEALTH value cluster.
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
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This workshop will guide you through using ArcGIS Online, finding datasets on ArcGIS Hub and the Living Atlas of the World, selecting Statistics Canada’s newly published proximity measures, and performing some basic Web mapping, analysis, and data enrichment.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
BackgroundGlobally, over 81 million people use e-cigarettes, and the majority of them are young adults. Using e-cigarettes causes different types of adverse health effects both in adults and elderly people. Over time, using e-cigarettes has detrimental consequences on lung function, brain development and numerous other illnesses.MethodsThis study employed a mixed-methods conducted between June and September 2023, comprising two phases: Geographical Information System (GIS) mapping of available e-cigarette point-of-sale (POS) locations and conducting 15 in-depth interviews (IDIs) with e-cigarette retailers, along with 5 key informant interviews (KIIs) involving tobacco control activists and policy experts. ArcGIS was employed for spatial analysis, creating distribution and type maps, and buffer and multi-buffer ring analyses were conducted to assess proximity to hospitals and academic institutions. Data analysis involved descriptive statistics for GIS mapping and qualitative analysis for interview transcripts, utilizing a priori codebook and thematic analysis.ResultsA total of 276 POS were mapped in the entire Dhaka city. About 55 POS were found within 100m distance from academic institutions in Dhaka city, which offers the easy accessibility of young generations to e-cigarettes. The younger generation is becoming the major target for e-cigarettes because of their alluring flavors, appealing looks, and variation in flavors. Sellers have been using different marketing tactics such as postering, offering discounts and using internet marketing on social media. Moreover, they try to convince the customers by saying that e-cigarettes are ‘not harmful’ or ‘less harmful’. However, retailers were mostly taking e-cigarettes from local wholesalers or distributors. Customers buy these products both from in-store and online services. Due to the absence of laws and regulations on e-cigarettes in Bangladesh, the availability, marketing, and selling of e-cigarettes are increasing alarmingly.ConclusionE-cigarette retail shops are mostly surrounded by academic institutions, and it is expanding. Besides, frequent exposure, easy accessibility, and tactful promotion encourage the younger generations to consume e-cigarettes. The government should take necessary control measures on manufacturing, storage, advertising, promotion, sponsorship, marketing, distribution, sale, import, and export in order to safeguard the health and safety of young and future generations.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
BackgroundGlobally, over 81 million people use e-cigarettes, and the majority of them are young adults. Using e-cigarettes causes different types of adverse health effects both in adults and elderly people. Over time, using e-cigarettes has detrimental consequences on lung function, brain development and numerous other illnesses.MethodsThis study employed a mixed-methods conducted between June and September 2023, comprising two phases: Geographical Information System (GIS) mapping of available e-cigarette point-of-sale (POS) locations and conducting 15 in-depth interviews (IDIs) with e-cigarette retailers, along with 5 key informant interviews (KIIs) involving tobacco control activists and policy experts. ArcGIS was employed for spatial analysis, creating distribution and type maps, and buffer and multi-buffer ring analyses were conducted to assess proximity to hospitals and academic institutions. Data analysis involved descriptive statistics for GIS mapping and qualitative analysis for interview transcripts, utilizing a priori codebook and thematic analysis.ResultsA total of 276 POS were mapped in the entire Dhaka city. About 55 POS were found within 100m distance from academic institutions in Dhaka city, which offers the easy accessibility of young generations to e-cigarettes. The younger generation is becoming the major target for e-cigarettes because of their alluring flavors, appealing looks, and variation in flavors. Sellers have been using different marketing tactics such as postering, offering discounts and using internet marketing on social media. Moreover, they try to convince the customers by saying that e-cigarettes are ‘not harmful’ or ‘less harmful’. However, retailers were mostly taking e-cigarettes from local wholesalers or distributors. Customers buy these products both from in-store and online services. Due to the absence of laws and regulations on e-cigarettes in Bangladesh, the availability, marketing, and selling of e-cigarettes are increasing alarmingly.ConclusionE-cigarette retail shops are mostly surrounded by academic institutions, and it is expanding. Besides, frequent exposure, easy accessibility, and tactful promotion encourage the younger generations to consume e-cigarettes. The government should take necessary control measures on manufacturing, storage, advertising, promotion, sponsorship, marketing, distribution, sale, import, and export in order to safeguard the health and safety of young and future generations.
This operation view contains services with shipping, maritime boundaries, and weather information for the west coast of the United States. The services in this web map are powered by ArcGIS GeoEvent Extension for Server and contain alerts for ships in certain boundaries, such as nature preserves, or inclement weather.Some of the widgets contained in this operation view are lists that sort the most important data such as those in geofences and those reporting with hazardous cargo. Data contained in this operation view includes:Maritime Boundaries and Port Information:Maritime Boundaries - Various maritime boundaries information provided by the National Oceanic and Atmospheric Administration (NOAAShipping Information:Proximity Alert - Generated buffer information created from an ArcGIS for GeoEvent Extension for Server processor of military vessels.Ship Position- Simulated shipping information obtained from the US Coast Guard (USCG).Weather Information:Meteorological Service of Environment Canada - Web map service with forecast, analysis, and observation layersforunderstanding current meteorological or oceanographic data.NOAA Lightning Strike Density - Time-enabled map service providing maps of experimental lightning strike density data.NOAA Weather Observations - Time-enabled map service providing map depicting the latest surface weather and marine weather observations.NOAA Weather Radar Mosaic - Time-enabled map service providing maps depicting mosaics of base reflectivity images across the United States.NOAA Weather Satellite Information - Time-enabled map service providing maps depicting visible, infrared, and water vapor imagery.
Gap Analysis Project (GAP) habitat maps are predictions of the spatial distribution of suitable environmental and land cover conditions within the United States for individual species. Mapped areas represent places where the environment is suitable for the species to occur (i.e. suitable to support one or more life history requirements for breeding, resting, or foraging), while areas not included in the map are those predicted to be unsuitable for the species. While the actual distributions of many species are likely to be habitat limited, suitable habitat will not always be occupied because of population dynamics and species interactions. Furthermore, these maps correspond to midscale characterizations of landscapes, but individual animals may deem areas to be unsuitable because of presence or absence of fine-scale features and characteristics that are not represented in our models (e.g. snags, vernal pools, shrubby undergrowth). These maps are intended to be used at a 1:100,000 or smaller map scale.These habitat maps are created by applying a deductive habitat model to remotely-sensed data layers within a species’ range. The deductive habitat models are built by compiling information on species’ habitat associations and entering it into a relational database. Information is compiled from the best available characterizations of species’ habitat, which included species accounts in books and databases, primary peer-reviewed literature. The literature references for each species are included in the "Species Habitat Model Report" and "Machine Readable Habitat Database Parameters" files attached to each habitat map item in the repository. For all species, the compiled habitat information is used by a biologist to determine which of the ecological systems and land use classes represented in the National Gap Analysis Project’s (GAP) Land Cover Map Ver. 1.0 that species is associated with. The name of the biologist who conducted the literature review and assembled the modeling parameters is shown as the "editor" type contact for each habitat map item in the repository.For many species, information on other mapped factors that define the environment that is suitable is also entered into the database. These factors included elevation (i.e. minimum, maximum), proximity to water features, proximity to wetlands, level of human development, forest ecotone width, and forest edge; and each of these factors corresponded to a data layer that is available during the map production. The individual datasets used in the modeling process with these parameters are also made available in the ScienceBase Repository (see the end of this Summary section for details). The "Machine Readable Habitat Database Parameters" JSON file attached to each species habitat map item has an "input_layers" object that contains the specific parameter names and references (via Digital Object Identifier) to the input data used with that parameter. The specific parameters for each species were output from the database used in the modeling and mapping process to the "Species Habitat Model Report" and "Machine Readable Habitat Database Parameters" files attached to each habitat map item in the repository.The maps are generated using a python script that queries the model parameters in the database; reclassifies the GAP Land Cover Ver 1.0 and ancillary data layers within the species’ range; and combines the reclassified layers to produce the final 30m resolution habitat map. Map output is, therefore, not only a reflection of the ecological systems that are selected in the habitat model, but also any other constraints in the model that are represented by the ancillary data layers.Modeling regions were used to stratify the conterminous U.S. into six regions (Northwest, Southwest, Great Plains, Upper Midwest, Southeast, and Northeast). These regions allowed for efficient processing of the species distribution models on smaller, ecologically homogenous extents.The 2008 start date for the models represents the shift in focus from state and regional project efforts to a national one. At that point all of the datasets needed to be standardized across the national extent and the species list derived based on the current understanding of the taxonomy. The end date for the individual models represents when the species model was considered complete, and therefore reflects the current knowledge related to that species concept and the habitat requirements for the species.Versioning, Naming Conventions and Codes: A composite version code is employed to allow the user to track the spatial extent, the date of the ground conditions, and the iteration of the data set for that extent/date. For example, CONUS_2001v1 represents the spatial extent of the conterminous US (CONUS), the ground condition year of 2001, and the first iteration (v1) for that extent/date. In many cases, a GAP species code is used in conjunction with the version code to identify specific data sets or files (i.e. Cooper’s Hawk Habitat Map named bCOHAx_CONUS_2001v1_HabMap).This collection represents the first complete compilation of terrestrial vertebrate species models for the conterminous U.S. based on 2001 ground conditions.The taxonomic concept for the species model being presented is identified through the Integrated Taxonomic Information System – Taxonomic Serial Number. To provide a link to the NatureServe species information the NatureServe Element Code is provided for each species. The identifiers included for each species habitat map item in the repository include references to a vocabulary system in ScienceBase where definitions can be found for each type of identifier.Input Datasets Used in Species Habitat Modeling: Links to the input datasets can be found in the Related External Resources section of this item. Please see the ScienceBase item for each input dataset for further explanation of its use in the modeling process. Each individual species habitat map in this collection includes the specific input layers and modeling parameters used for that model in its parameters file.USER CONSTRAINTS: It is strongly recommended that these data are directly acquired from the U.S. Geological Survey and not indirectly through other sources, which may have modified the data in some way. The Digital Object Identifiers for each species habitat map provide the persistent reference that should be used to obtain the maps for use. While these data were produced in support of the National Gap Analysis Project, it is expected that they would be used for other applications; therefore, we list below both appropriate and inappropriate uses. For many uses, it is unlikely that this dataset will provide the only data needed, and for uses with a regulatory outcome, field surveys should be conducted to verify the result. These models represent predictions of the locations of suitable habitat for the species and are not models of species occupancy. There are many reasons why a habitat may not be occupied at a particular point in time. Additionally, these maps should not necessarily supersede existing distribution models for species of management concern. This includes smaller scale, regional maps for populations of species modeled by USGS.Appropriate uses of the data: Primarily as a coarse map for a large area such as a state or to provide context for finer-level maps. A general list of possible applications include:National, regional or statewide biodiversity planningNational, Regional or state habitat conservation planningLarge-area resource management planningCoarse-filter evaluation of potential impacts or benefits of major projects or plan initiatives on biodiversity, such as utility or transportation corridors, wilderness proposals, habitat connectivity proposals, climate change adaptation proposals, regional open space and recreation proposals, etc.Determining relative amounts of management responsibility for specific biological resources among land stewards to facilitate cooperative management and planningBasic research on regional distributions of plants and animals and to help target both specific species and geographic areas for needed researchEnvironmental impact assessment for large projects or military activitiesEstimation of potential economic impacts from loss of biological resource-based activitiesEducation at all levels and for both students and citizensInappropriate Uses: Examples include:Using the data to map small areas (less than thousands of hectares), typically requiring mapping at 1:24,000 scale and using aerial photographs or ground surveysCombining these data with other data finer than 1:100,000 scale to produce new hybrid maps or answer queriesEstablishing exact boundaries for regulation or acquisitionEstablishing definite occurrence or non-occurrence of a species for an exact geographic areaDetermining abundance, health, or condition of a speciesEstablishing a measure of accuracy of other species models by comparison with the Gap Analysis Project species distribution modelsUsing the data without acquiring and reviewing the metadata
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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.