1. Hotspot analysis is a commonly used method in ecology and conservation to identify areas of high biodiversity or conservation concern. However, delineating and mapping hotspots is subjective and various approaches can lead to different conclusions with regard to the classification of particular areas as hotspots, complicating long-term conservation planning. 2. We present a comparative analysis of recent approaches for identifying waterbird hotspots, with the goal of developing insights about the appropriate use of these methods. We selected four commonly used measures to identify persistent areas of high use: kernel density estimation, Getis-Ord Gi*, hotspot persistence, and hotspots conditional on presence, which represent the range of quantitative hotspot estimation approaches used in waterbird analyses. We applied each of the methods to aerial survey waterbird count data collected in the Great Lakes from 2012-2014. For each approach, we identified areas of high use for seven species/species groups and then compared the results across all methods and with mean effort-corrected counts. 3. Our results indicate that formal hotspot analysis frameworks do not always lead to the same conclusions. The kernel density and Getis-Ord Gi* methods yielded the most similar results across all species analyzed and were generally correlated with mean effort-corrected count data. We found that these two models can differ substantially from the hotspot persistence and hotspots conditional on presence estimation approaches, which were not consistently similar to one another. The hotspot persistence approach differed most significantly from the other methods but is the only method to explicitly account for temporal variation. 4. We recommend considering the ecological question and scale of conservation or management activities prior to designing survey methodologies. Deciding the appropriate definition and scale for analysis is critical for interpretation of hotspot analysis results as is inclusion of important covariates. Combining hotspot analysis methods using an integrative approach, either within a single analysis or post-hoc, could lead to greater consistency in the identification of waterbird hotspots. alldata_attributed_v04122017More information can be found on the Midwest Avian Data Center, a regional node of the Avian Knowledge Network, hosted by Point Blue Conservation Science (http://data.pointblue.org/partners/mwadc/) or through the Zipkin Quantitative Ecology Lab GitHub page (https://zipkinlab.github.io/).

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 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.

The following report outlines the workflow used to optimize your Find Hot Spots result:There were 866 valid input features.WHITE Properties:Min0.0000Max9979.0000Mean1405.8406Std. Dev.1754.9340There were 10 outlier locations; these were not 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: 2551.0000 Meters.Hot Spot AnalysisThere are 700 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high WHITE values cluster.Blue output features represent cold spots where low WHITE values cluster.

Nauru is a small country comprising a single island with an area of only 22 km2. The island is having severe difficulties in achieving a safe and adequate supply of potable water and suffers from pollution of local groundwater due to inadequate sanitation services. The problems have arisen from the collapse of the utility services when phosphate mining ceased, followed by a national financial crisis. Available online Call Number: [EL] Physical Description: 6 Pages

Comparison of hot spot analysis results with and without FDR correction.

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

Summary of geographically weighted regression analysis result and model comparisons.

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.

Marine debris poses significant threats to coastal ecosystems and infrastructure, especially in semi-enclosed regions where monitoring is limited by inaccessibility and uneven population distribution. In Howe Sound, British Columbia, this study integrates remote sensing and environmental modeling to predict debris accumulation zones. Sentinel-2 satellite imagery was selected due to its high spatial resolution, broad spectral range, and frequent revisit time, which make it well-suited for capturing detailed coastal features. A neural network algorithm was used to classify six landcover types with an overall accuracy of 0.98. The classification results showed that many known debris hotspots are located near urban shorelines and within semi-enclosed bays. To simulate debris transport, river discharge and seasonal wind direction were modeled as surface movement drivers. The study area was divided into three sections to account for spatial variation in debris driving forces contribution. Hourly wind data from four weather stations were used to construct wind rose diagrams that captured seasonal changes in wind direction. The simulation identified 49 predicted debris hotspot locations. Of these, 20 overlapped with known hotspots, while 10 of the 29 newly identified hotspots are in less populated and previously underreported areas, particularly along the western shoreline. These findings demonstrate that remote sensing, when combined with physically based modeling, can overcome limitations of traditional monitoring methods and improve the identification of marine debris accumulation. This approach provides a scalable and transferable framework for supporting more targeted and proactive coastal management strategies.

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.

The rehabilitation of degraded subtropical natural forests is a global concern. A detailed assessment of their structure is a challenging and costly prerequisite because diverse structures exist depending on the cause and degree of degradation. Recent remote sensing concepts and technologies provide a detailed picture of actual forest structure, even in difficult terrain. When it comes to planning and implementing rehabilitation measures on the ground, however, meaningful forest management units (FMUs) must be created that are large enough to allow technical implementation, but which are also homogenous in structure. To date, the delineation of FMUs has, in most cases, been achieved qualitatively based on expert knowledge. The aim of this contribution is to develop and demonstrate a method for creating and delineating meaningful FMUs based on quantitative information acquired from remote sensing and spatial statistics. Therefore, a case study was conducted in a 3940-ha fire-degraded forest area in the Argentinean cloud forest of Yungas Pedemontana. A plot-based field inventory and an aerial survey with an unmanned aerial vehicle were conducted. The Adjusted Canopy Coverage Index (ACCI), as a metric for stand structure, was formulated to predict basal area from canopy height models. A SPOT6 image of the area was object-based segmented and classified into four fire-severity strata by training it with the ACCI values. The resulting classification presented a mosaic pattern in which the stands are homogenous but far too small (average 3129 m2) for planning adaptive management. Therefore, features in close proximity with similar structure (i.e. ACCI values) were aggregated using the Hot Spot Analysis (Getis-Ord Gi*) tool from the Arc geographic information system environment to create FMUs. Clusters were calculated at four scales: 10, 20, 30 and 40 ha (resulting in threshold radii of 178, 252, 309 and 357 m, respectively), using ACCI values as the variable of aggregation. As a result, average cluster areas were obtained of 33.9 ha for the shortest threshold distance of analysis and 138.5 ha for the greatest threshold distance. The tool significantly aggregated between 30.7% and 60.8% of the area into either coldspots or hotspots of ACCI, facilitating the delineation of FMUs for the planning of adaptive rehabilitation measures. There is a trade-off, however, between the gain in area of the FMUs and the loss of homogeneity: for a 357 m distance threshold, 12% more of the area was misclassified, compared with a 178 m threshold.

This research paper presents a globally replicable methodology for subnational hotspot analysis of mangrove restoration suitability. The study utilized Central America as a focal area and employed a two-phase workflow involving scripted analysis in RStudio and non-scripted application of QGIS geoprocessing tools and qualitative assessment. Approaches to spatially defining mangrove areas for analysis were examined, including global administrative zones, buffering around mangrove areas of loss, and manual boundary selection. Specific datasets for restoration suitability indicators such as mangrove loss, population distribution, poverty metrics, soil organic carbon, protected areas and others were evaluated for effectiveness. Key findings included high restoration suitability in Nicaragua and Honduras, consistent underestimation of mangrove loss to aquaculture conversion, and varying effectiveness of protected areas between countries and designation types. The discussion section expands on the effectiveness of different indicators, compares mangrove delineation methods from the literature, emphasizes the usefulness of screening processes, and suggests future directions for restoration hotspot analysis. Overall, this research presents a flexible hotspot analysis methodology suitable for restoration practitioners operating within common constraints such as open-source software and freely accessible data.

Market Overview: The global USB hotspot market is projected to grow from USD XX million in 2025 to USD XX million by 2033, at a CAGR of XX%. The market is driven by factors such as the increasing demand for mobile data connectivity, the proliferation of internet-connected devices, and the growing adoption of remote work and education. North America and Europe are the dominant regions in the market, with significant contributions from China and India in the Asia Pacific region. Segmentation and Competition: The USB hotspot market is segmented based on application into mobile devices, laptops, and tablets. By type, the market is divided into 3G, 4G, and 5G hotspots. Major players in the market include TP-Link, Huawei, Netgear, ZTE, D-Link, Sierra Wireless, Novatel Wireless, FORTRESS, XCom Global Inc., Inseego, Fiberhome, Tozed Kangwei, Wistron NeWeb Corporation (WNC), Askey Computer, and Zyxel. These companies engage in various strategies such as product innovation, strategic partnerships, and geographical expansion to gain competitive advantage.

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.

The global market for mobile WiFi hotspots is expanding rapidly, driven by rising demand for internet connectivity on the go. The market is expected to reach a value of $XX million by 2033, growing at a CAGR of XX% over the forecast period. The primary drivers of this growth include the increasing proliferation of smartphones, tablets, and other mobile devices, as well as the growing popularity of streaming media and online gaming. Key industry trends include the emergence of 5G technology, which is expected to further enhance the performance and capabilities of mobile WiFi hotspots. Other notable trends include the increasing adoption of cloud-based services and the growing use of mobile WiFi hotspots for business applications. The competitive landscape of the market is fragmented, with a number of major players such as Huawei, TP-Link, D-Link, Inseego, NETGEAR, Franklin Wireless, Samsung, ZTE, Alcatel, and Belkin. This comprehensive report provides a thorough analysis of the global Mobile WiFi Hotspot market, with a detailed examination of its key segments, trends, and industry dynamics. The report offers valuable insights into the market's competitive landscape, growth catalysts, and emerging trends.

The dataset contains four nighttime lights satellite imageries as the source data, which were taken from Wuhan Ccity on June 14, 2018, Wuhan City on September 15, 2018, Shenyang City on September 10, 2018 and Shenyang City on March 17, 2019. The dataset also provides the "result.mxd" file for urban commercial areas detection using these four imageries .

Market Overview The global Wi-Fi hotspot market is projected to reach a market size of XX million USD by 2033, exhibiting a CAGR of XX% over the forecast period (2023-2033). The growing demand for seamless internet connectivity, increasing smartphone penetration, and advancements in IoT technology are key drivers of market growth. The market is segmented by type (wireless hotspot gateways, wireless hotspot controllers, mobile hotspot devices) and application (hospital, retail sectors, financial services, education, others). Competitive Landscape and Regional Analysis Key players in the Wi-Fi hotspot market include Ipass, Ubiquiti Networks, Nokia Networks, Boingo Wireless, and Netgear. These companies compete on factors such as product innovation, geographical reach, and customer support. The market is concentrated in North America and Europe, with emerging markets in Asia-Pacific and Latin America expected to drive growth in the coming years. Regional factors such as government regulations, infrastructure development, and consumer spending patterns will influence market dynamics. The report provides an in-depth analysis of the competitive landscape and potential opportunities for market participants.

The global mobile WiFi hotspot market is projected to reach USD 60.3 billion by 2033, growing at a CAGR of 8.2% from 2025 to 2033. The rise in demand for mobile data and internet connectivity in remote areas, the growing popularity of IoT devices, and the increasing penetration of smartphones and tablets are key factors driving market growth. Additionally, the emergence of cloud-based services and the proliferation of streaming media content further augment the demand for mobile WiFi hotspots. Asia Pacific is anticipated to hold the largest market share during the forecast period due to the rapid adoption of mobile devices, increasing internet penetration, and the presence of a large population in developing countries. North America is expected to witness significant growth owing to the high adoption rate of advanced technologies, the presence of a mature market for wireless communication, and the growing demand for mobile data. The Middle East and Africa region is projected to exhibit a steady growth rate due to the increasing demand for mobile internet connectivity and the expansion of mobile broadband networks.

This dataset includes counts of mortalities, hot spot z-scores and p-values for each of the time blocks used in the study.