The Minnesota DNR Toolbox provides a number of convenience geoprocessing tools used regularly by MNDNR staff. Many of these may be useful to the wider public. However, some tools may rely on data that is not available outside of the DNR.

Toolsets included in MNDNR Tools:
- Analysis Tools
- Conversion Tools
- General Tools
- LiDAR and DEM Tools
- Sampling Tools

The application download includes a comprehensive help document, which you can also access separately here: ArcGISPro_MNDNR_Toolbox_Pro_User_Guide.pdf

These toolboxes are provided free of charge and are not warrantied for any specific use. We do not provide support or assistance in downloading or using these tools. We do, however, strive to produce high-quality tools and appreciate comments you have about them.

Your manager has just assigned you to help the Park Service select some new observation points within Dinosaur National Park. These new observation points should meet a set of criteria based on their location. Twenty potential observation points have been identified. So, what is your next step? How can you use ArcGIS Pro to accomplish the analysis efficiently and accurately?After completing this course, you will be able to perform the following tasks:Use the appropriate geoprocessing tool for a given spatial problem.Demonstrate multiple methods for accessing geoprocessing tools.Use ArcGIS Pro to set geoprocessing environments.

Python Scripting for ArcGIS Pro stars with the fundamentals of Python programming and then dives into how to write useful Python scripts that work with spatial data in ArcGIS Pro. Leam how to execute geoprocessing tools, describe, create and update data, as well as execute a number of specialized tasks. See how to write simple, Custom scripts that will automate your ArcGIS Pro workflows.Some of the key topics you Will learn include:Python fundamentalsSetting up a Python editorAutomating geoprocessing tasksExploring and manipulating spatal and tabular dataWorking With geometriesMap scriptingDebugging ard error handlingHelpful "points to remember," key terms, and review questions are included at the end of each chapter to reinforce your understanding of Python. Corresponding data and exercises are available online.Whether want to learn python or already have some experience, Python Scripting for ArcGlS Pro is comprehensive, hands-on book for learning versatility of Python coding as an approach to solving problems and increasing your productivity in ArcGlS Pro. Follow the step-by-step instruction and common workflow guidance for automating tasks and scripting with Python.Don't forget to also check out Esri Press's other Python title:Advanced Python Scripting for ArcGIS ProAUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPaul A Zandbergen is an associate professor of geography at the University of New Mexico in Albuquerque. His areas of expertise include geographic information science; spatial and statistical analysis techniques using GIS; error and uncertainty in spatial data; GIS applications in criminology, economics, health, and spatial ecology; terrain analysis and modeling; and community-based mapping using GIS and GPS.Pub Date: Print 7/7/2020 Digital: 7/7/2020ISBN: Print 9781589484993 Digital: 9781589485006 Price: Print: $79.99 USD Digital: $79.99 USD Pages: 420 Trim: 8 x 10 in.Table of ContentsPrefaceAcknowledgmentsChapter 1. Introducing Py%onChapter 2. Working with Python editorsChapter 3. Geoprocessing in ArcGIS ProChapter 4. Leaming Python language fundamentalsChapter 5. Geoprocessing using PythonChapter 6. Exploring spatial dataChapter 7. Debugging and error handlingChapter 8. Manipulating spatial and tabular dataChapter 9. Working with geometriesChapter 10. Working with rastersChapter 11. Map scriptingIndexPython Scripting and Advanced Python Scripting for ArcGIS Pro | Official Trailer | 2020-07-12 | 01:04Paul Zandbergen | Interview with Esri Press | 2020-07-10 | 25:37 | Link.

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

Although there are a large number of software products available for calculating landscape metrics (e.g. FRAGSTATS, landscapemetrics package in R) no tools are currently available (to my knowledge) that calculate landscape metrics directly in ArcGIS Pro. Moreover, many, if not most, landscape metrics were designed with vector data in mind, but most software calculates landscape metrics from raster data due to processing time and complexity. Scaling landscape metrics can also be tedious in some instances. This toolbox was designed to calculate attributes of patches that are easily calculated on polygons in ArcGIS (i.e. area, number of patches, Landscape Shape Index, edge density, patch size, distance to the nearest patch) and scales those calculations to coarser resolutions using Block Statistics. The tool also summarizes the relationships among metrics by using Principal Component Analysis and correlation matrices to assess relationships among variables. All variables are output to a single folder.

The Habitat Suitability Analysis was built using ArcGIS Pro's ModelBuilder tool. This program does not have an option to save the model's inputs as a relative file path. As a result, the model may not run because it's searching for each layer's original file path. If this happens, we have included a file titled Habitat_Suitability_Analysis_Script that outlines the processes we used to build the model. This submission contains three folders and three supplemental files. The folder titled "Data" includes all of the raw data and data input in the Habitat Suitability Analysis. The folder titled "Scripts" describes the steps to build the Habitat Suitability Analysis model in ArcGIS Pro. The Results folder contains the Habitat Suitability Analysis model and the data that was input into the model. The supplemental files are a file titled "Dryad_Folder_Contents" which describes the contents of every folder in this submission, and a file titled "Habitat_Suitability_Analysis_README" which contain...

The market for GIS Mapping Tools is projected to reach a value of $XX million by 2033, growing at a CAGR of XX% during the forecast period (2025-2033). The market growth is attributed to the increasing adoption of GIS mapping tools by various industries, including government, utilities, and telecom, for a wide range of applications such as geological exploration, water conservancy projects, and urban planning. The convergence of GIS with other technologies such as artificial intelligence (AI) and the Internet of Things (IoT) is further driving market growth, as these technologies enable GIS mapping tools to provide more accurate and real-time data analysis. The market is segmented by type (cloud-based, web-based), application (geological exploration, water conservancy projects, urban planning, others), and region (North America, Europe, Asia Pacific, Middle East & Africa). North America is expected to remain the largest market for GIS mapping tools throughout the forecast period, due to the early adoption of these technologies and the presence of leading vendors such as Esri, MapInfo, and Autodesk. Asia Pacific is expected to experience the highest growth rate during the forecast period, due to the increasing adoption of GIS mapping tools in emerging economies such as China and India. Key industry players include Golden Software Surfer, Geoway, QGIS, GRASS GIS, Google Earth Pro, CARTO, Maptive, Shenzhen Edraw Software, MapGIS, Oasis montaj, DIVA-GIS, Esri, MapInfo, Autodesk, BatchGeo, Cadcorp, Hexagon, Mapbox, Trimble, and ArcGIS.

The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

Over 40,000 road crossings in Maine are maintained by Maine Department of Transportation (MaineDOT) managers, emergency managers, natural resource planners, and municipalities. Resource managers need a way to quickly and comprehensively assess, during the planning stages of potential transportation-related projects, how ecological, hydrologic, and structural characteristics of bridges and culverts and their watersheds could adversely affect project schedules and budgets. Factors that are critical to evaluate and incorporate into overall assessments of project risk include basin, land-use, and climatic characteristics; vulnerability to specific events, such as floods; and complicating factors in the watershed, such as endangered species, evacuation routes, and historical sites. A Python script tool has been built for ArcGIS Pro as an automated screening tool that draws on existing geographic information system (GIS) data layers to identify potential risk factors and quantify risk ...

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes wheat production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Wheat ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United StatesVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Area Harvested in AcresOperations with Area HarvestedOperations with SalesProduction in BushelsSales in US DollarsIrrigated Area Harvested in AcresOperations with Irrigated Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.Many other ready-to-use layers derived from the Census of Agriculture can be found in the Living Atlas Agriculture of the USA group.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

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

Dissertation and dataset present an archaeological study of the Huarmey Valley region, located on the northern coast of Peru. My work uses modern and innovative digital methods. My research focuses on better understanding the location of one of the most important sites in the valley, Castillo de Huarmey, by learning about the context in which it functioned. The Imperial Mausoleum located at the site, along with the burial chamber beneath it, is considered one of the most important discoveries regarding the Wari culture in recent years.In the dissertation, I address issues concerning both the location of the site on a macro scale - in the entire Huarmey Valley, on a micro scale - the context of the Huarmey Valley delta – and the spatial relationships within the burial chamber located beneath the Mausoleum. I ask the questions (i) How did Castillo de Huarmey communicate with other sites dated to the same period located in the valley and also in adjacent valleys? Did this influence its role in the region? (ii) Is the Mausoleum at Castillo de Huarmey located intentionally and what was the meaning of this location at the micro and macro scale? (iii) What spatial relations existed between Castillo de Huarmey and other sites from the same period? (iv) Does the position of the artifacts, found in situ in the burial chamber, show important relationships between buried individuals? (v) Does spatial analysis show interesting spatial patterns within the burial inside the chamber?The questions can be answered by describing and testing the digital methods proposed in the doctoral dissertation related to both field data collection and their analysis and interpretation. These methods were selected and adapted to a specific area (the Northern Coast of Peru) and to the objective of answering the questions posed in the thesis. The wide range of digital methods used in archaeology is made possible by the use of Geographic Information Systems (abbreviated GIS) in research. To date, GIS in archaeology is used in three aspects (Wheatley and Gillings 2002): (i) statistical and spatial analysis to obtain new information, (ii) landscape archaeology, and (iii) Cultural Resource Management.My dissertation is divided into three main components that discuss the types of digital methods used in archaeology. The division of these methods will be adapted to the level of detail of the research (from the location of the site in the region, to the delta of the Huarmey Valley, to the burial chamber of the Mausoleum) and to the way they are used in archaeology (from Cultural Resource Management, to archaeological landscape analysis, to statistical-spatial analysis). One of the aims of the dissertation is to show the methodological path of the use of digital methods, i.e. from the acquisition of data in the field, through analysis, to their interpretation in a cultural context. However, the main objective of my research is to interpret the spatial relationships from the macro to the micro level, in the case described, against the background of other sites located in the valley, the location of Castillo de Huarmey in the context of the valley delta, and finally to the burial chamber of the Mausoleum. The uniqueness of the described burial makes the research and its results pioneering in nature.As a final result of my work I would like to determine whether relationships can be demonstrated between the women buried in the burial chamber and whether the location of particular categories of artifacts can illustrate specific spatial patterns of burial. Furthermore, my goal is to attempt to understand the relationship between the Imperial Mausoleum and other sites (archival as well as newly discovered) located in the Huarmey Valley and to understand the role of the site's location.Published dataset represents, described in the dissertation, mobile GIS survey on the site PV35-5 created in Survey123, ESRI application; xml and xls used for creating the survey that was used during the research of the site, as well as the results of the survey published in ArcGIS Pro package. The package includes collected data as points, saved as .shp, as well as ortophotomaps (as geotiff) and Digital Elevation Model and hillshade of PV35-5. The published dataset represents part of the dissertation describing archaeological landscape analysis of Huarmey Valley’s delta.

Predator-prey interactions can be profoundly influenced by vegetation conditions, particularly when predator and prey prefer different habitats. Although such interactions have proven challenging to study for small and cryptic predators, recent methodological advances substantially improve opportunities for understanding how vegetation influences prey acquisition and strengthen conservation planning for this group. The California Spotted Owl (Strix occidentalis occidentalis) is well-known as an old-forest species of conservation concern, but whose primary prey in many regions – woodrats (Neotoma spp.) – occurs in a broad range of vegetation conditions. Here, we used high-resolution GPS tracking coupled with nest video monitoring to test the hypothesis that prey capture rates vary as a function of vegetation structure and heterogeneity, with emergent, reproductive consequences for Spotted Owls in Southern California. Foraging owls were more successful capturing prey, including woodrats, in taller multilayered forests, in areas with higher heterogeneity in vegetation types, and near forest-chaparral edges. Consistent with these findings, Spotted Owls delivered prey items more frequently to nests in territories with greater heterogeneity in vegetation types and delivered prey biomass at a higher rate in territories with more forest-chaparral edge. Spotted Owls had higher reproductive success in territories with higher mean canopy cover, taller trees, and more shrubby vegetation. Collectively, our results provide additional and compelling evidence that a mosaic of large tree forests with complex canopy and shrubby vegetation increases access to prey with potential reproductive benefits to Spotted Owls in landscapes where woodrats are a primary prey item. We suggest that forest management activities that enhance forest structure and vegetation heterogeneity could help curb declining Spotted Owl populations while promoting resilient ecosystems in some regions. Methods See README DOCUMENT Naming conventions *RSF or prey refers to prey capture analysis *delivery in a file name refers to delivery rate analysis *repro in a filename means that file is for the delivery rate analysis

Setup *files with vegetation data should work with minimal alteration(will need to specify working directory) with associated R code for each analysis *Shapefiles were made in ArcGIS pro but they can be opened with any GIS software such as QGIS.

Locational data files

NOTE LOCATIONAL DATA IS SHIFTED AND ROTATED FROM THE ORIGINAL -due to the sensitive nature of this species. The locational_data includes: * All_2021_owls_shifted * Point file showing all GPS tag locations for prey capture analysis * Attributes include: * TERRITORY ID: Numerical identifier for each bird * Year: year GPS tag was recorded * Month: month GPS tag was recorded * Day: Day GPS tag was recorded * Hour: Hour GPS tag was recorded * Minute: minute GPS tag was recorded * All_linked_polygons_shifted * Polygon file showing capture polygons for prey capture analysis * Attributes include * Territory ID: numerical identifier for each bird * Polygon id: numerical identifier for each capture polygon for each bird * Shape area: area of each polygon * SBNF_camera_nests_shifted * Point file showing spotted owl nests for prey capture analysis * Attributes include * Territory id: numerical identifier for each bird * C95_KDE_2021_socal_shifted * Polygon file of owls 95% kernel density estimate for prey delivery rate analysis * Attributes include * Id: numerical identifier for each territory(bird) * Area: area of each polygon * San_bernardino_territory_centers * Point file showing Territory centers for historical SBNF territories – shifted for repro success analysis * Attributes include * Repro Territory id: unique identifier for each territory in broader set of territories

Besides the sifted locational data we have included - For the Resource selection function vegetation data, for the delivery analysis we have included an overview of prey deliveries by territory and vegetation data used, and for the reproductive analysis we have again included vegetation data as well as an overview of reproductive success. these are labled as follows:

Files for the prey capture analysis

Socal_RSF_data.txt

*description: Text file with vegetation data paired with capture locations both buffered polygons used in prey capture analysis and the unbuffered ones which were not used.(Pair with Socal_rsf_code R script) *format: .txt *Dimensions: 2641 X 35

*Variables: *ORIG_fid: completely unique identifier for each row *unique_id: unique identifier for each capture polygon(shared between a buffered capture location and its unbuffered pair) *territory_id: unique numerical idenifier of territory *Polygon_id: within territory unique prey capture polygon id *buff: bianary buffered or unbuffered (1=buffered, 0=unbuffered) *used: bianary used=1 available=0 *prey_type: prey species associated with polygon unkn:unknown, flsq:flying squirel, wora:woodrat, umou:mouse, pogo:pocketgopher, grsq: grey squirel, ubrd: unknown bird, umol:unknown mole, uvol, unknown vole. *area_sqm: area of polygon in square meters *CanCov_2020_buff: average canopy cover in polygon *CanHeight_2020_buff: average canopy height in polygon *Canlayer_2020_buff: average number of canopy layers in polygon *Understory_density_2020_buff: average brushy vegetation density in polygon *pix_COUNT: count of pixels in polygon (not needed for analysis) *p_chaparral: percent of polygon comprised of chaparral habitat
*p_conifer: percent of polygon comprised of conifer habitat *p_hardwood: percent of polygon comprised of hardwood habitat *p_other: percent of polygon comprised of other habitat types *Calveg_cap_CHt_gt10_CC_30to70_intersect_buff: percent of polygon comprised of trees taller than 10m with 30-70percent canopy cover (used to check data) *Calveg_cap_CHt_gt10_CCgt70_intersect_buff: percent of polygon comprised of trees taller than 10m with greater than 70percent canopy cover (used to check data) *Calveg_cap_CHt_lt10_intersect_buff:percent of polygon comprised of trees less than 10m (used to check data)
*p_sm_conifer: percent of polygon comprised of conifer trees less than 10m (used to calculate diversity)
*p_lrg_conifer_sc: percent of polygon comprised of conifer forests >10m tall with sparse canopy(used to calculate diversity) *p_large_conifer_dc: percent of polygon comprised of conifer forests greater than 10m tall with dense canopy (used to calculate diversity) *p_sm_hard: percent of polygon comprised of hardwood trees less than 10m (used to calculate diversity) *p_lrg_hard_sc: percent of polygon comprised of hardwood forests greater than 10m with sparse canopy(used to calculate diversity)
*p_lrg_hard_dc: percent of polygon comprised of hardwood forests greater than 10m dense canopy (used to calculate diversity) *p_forests_gt10_verysparse_CC: percent of polygon comprised of trees less than 10m with very sparse canopies (used to calculate diversity) *primary_edge: total distance in meters of primary edge in a polygon
*normalized_by_area_primary_edge: total distance in m of primary edge in a polygon divided by the area of the polygon
*secondary_edge: total distance in meters of secondary edge in a polygon *normalized_by_area_secondary_edge:total distance in m of secondary edge in a polygon divided by the area of the polygon *coarse_diversity: shannon diversity in each polygon (see methods below) *fine_diversity: shannon diversity in each polygon (see methods below) *nest_distance: distance from polygon center to nest for each polygon in meters

For the Delivery analysis

note: For information on determining average prey biomass see methods as well as zulla et al 2022 for flying squirels and woodrat masses Zulla CJ, Jones GM, Kramer HA, Keane JJ, Roberts KN, Dotters BP, Sawyer SC, Whitmore SA, Berigan WJ, Kelly KG, Gutiérrez RJ, Peery MZ. Forest heterogeneity outweighs movement costs by enhancing hunting success and fitness in spotted owls. doi:10.21203/rs.3.rs-1370884/v1. PPR:PPR470028.

prey_deliveries_byterritory.csv *Description: overview file of prey delivered to each nest *format: .csv *dimensions:332 x 8

*Variables: *SITE: Unique numerical identifier for each territory *DATE: date prey was delivered (in UTC) *CAMERA TIME: time in UTC prey was delivered *VIDEO TIME: time on video prey was delivered - unrelated to real time just original file
*PREY ITEM: prey species delivered to nest unkn:unknown, uncr: unknown if delivery(removed from eventual analysis due to

The Travel Time Tool was created by the MN DNR to use GIS analysis for calculation of hydraulic travel time from gridded surfaces and develop a downstream travel time raster for each cell in a watershed. This hydraulic travel time process, known as Time of Concentration, is a concept from the science of hydrology that measures watershed response to a precipitation event. The analysis uses watershed characteristics such as land-use, geology, channel shape, surface roughness, and topography to measure time of travel for water. Described as Travel Time, it calculates the elapsed time for a simulated drop of water to migrate from its source along a hydraulic path across different surfaces of the replicated watershed landscape, ultimately reaching the watershed outlet. The Travel Time Tool creates a raster whereas each cell is a measure of the length of time (in seconds) that it takes water to flow across it, and then accumulates the time (in hours) from the cell to the outlet of the watershed.

The Travel Time Tool creates an impedance raster from Manning's Equation that determines the velocity of water flowing across the cell as a measure of time (in feet per second). The Flow Length Tool uses the travel time Grid for the impedance factor and determines the downstream flow time from each cell to the outlet of the watershed.

The toolbox works with ArcMap 10.6.1 and newer and ArcGIS Pro.

For step-by-step instructions on how to use the tool, please view MN DNR Travel Time Guidance.pdf

The global GIS mapping tools market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033, reaching approximately $39 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising adoption of cloud-based GIS solutions offers enhanced accessibility, scalability, and cost-effectiveness, particularly appealing to smaller organizations. Secondly, the burgeoning need for precise spatial data analysis in various applications, including urban planning, geological exploration, and water resource management, significantly contributes to market growth. Thirdly, advancements in technologies such as AI and machine learning are integrating into GIS tools, leading to more sophisticated analytical capabilities and improved decision-making. Finally, the increasing availability of high-resolution satellite imagery and other geospatial data further fuels market expansion. However, market growth is not without challenges. High initial investment costs associated with implementing and maintaining sophisticated GIS systems can pose a barrier to entry for smaller businesses. Furthermore, the complexity of GIS software and the need for specialized skills to operate and interpret data effectively can limit widespread adoption. Despite these restraints, the market’s overall trajectory remains positive, with the cloud-based segment projected to maintain a dominant market share due to its inherent advantages. Growth will be geographically diverse, with North America and Europe continuing to be significant markets, while Asia-Pacific is expected to experience the fastest growth due to rapid urbanization and infrastructure development. The continued development of user-friendly interfaces and increased integration with other business intelligence tools will further accelerate market expansion in the coming years.

This dataset offers a detailed inventory of road intersections and their corresponding suburbs within Cape Town, meticulously curated to highlight instances of high pedestrian crash counts observed in "high-high" cluster and "high-low" outlier fishnet grid cells across the years 2017, 2018, 2019, and 2021. To enhance its utility, the dataset meticulously colour-codes each month associated with elevated crash occurrences, providing a nuanced perspective. Furthermore, the dataset categorises road intersections based on their placement within "high-high" clusters (marked with pink tabs) or "high-low" outlier cells (indicated by red tabs). For ease of navigation, the intersections are further organised alphabetically by suburb name, ensuring accessibility and clarity.Data SpecificsData Type: Geospatial-temporal categorical data with numeric attributesFile Format: Word document (.docx)Size: 255 KBNumber of Files: The dataset contains a total of 264 road intersection records (68 "high-high" clusters and 196 "high-low" outliers)Date Created: 21st May 2024MethodologyData Collection Method: The descriptive road traffic crash data per crash victim involved in the crashes was obtained from the City of Cape Town Network InformationSoftware: ArcGIS Pro, Open Refine, Python, SQLProcessing Steps: The raw road traffic crash data underwent a comprehensive refining process using Python software to ensure its accuracy and consistency. Following this, duplicates were eliminated to retain only one entry per crash incident. Subsequently, the data underwent further refinement with Open Refine software, focusing specifically on isolating unique crash descriptions for subsequent geocoding in ArcGIS Pro. Notably, during this process, only the road intersection crashes were retained, as they were the only incidents with spatial definitions.Once geocoded, road intersection crashes that involved a pedestrian were extracted so that subsequent spatio-temporal analyses would focus on these crashes only. The spatio-temporal analysis methods by which the pedestrian crashes were analysed included spatial autocorrelation, hotspot analysis, and cluster and outlier analysis. Leveraging these methods, road intersections involving pedestrian crashes identified as either "high-high" clusters or "high-low" outliers were extracted for inclusion in the dataset.Geospatial InformationSpatial Coverage:West Bounding Coordinate: 18°20'EEast Bounding Coordinate: 19°05'ENorth Bounding Coordinate: 33°25'SSouth Bounding Coordinate: 34°25'SCoordinate System: South African Reference System (Lo19) using the Universal Transverse Mercator projectionTemporal InformationTemporal Coverage:Start Date: 01/01/2017End Date: 31/12/2021 (2020 data omitted)

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes corn production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Corn ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Operations with SalesSales in US DollarsGrain - Area Harvested in AcresGrain - Operations with Area HarvestedGrain - Production in BushelsGrain - Irrigated Area Harvested in AcresGrain - Operations with Irrigated Area HarvestedSilage - Area Harvested in AcresSilage - Operations with Area HarvestedSilage - Production in TonsSilage - Irrigated Area Harvested in AcresSilage - Operations with Area HarvestedTraditional or Indian - Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedTraditional or Indian - Production in PoundsTraditional or Indian - Irrigated Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

Workflow for Line-of-Sight (LOS) analysis in GIS (ArcGIS Desktop/PRO software release): the video shows the setup of hypothetical observing points, evenly distributed through the space of the virtually reconstructed house of Caecilius Iucundus (height on the ground floor 1.65 m, space interval 0.2 m). LOS algorithm enabled us to generate a vertical map featuring the percentage of visual exposure of the fresco’s surface and a horizontal map of cumulative visibility of the fresco from each observing location