ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)

**When using the GIS data included in these map packages, please cite all of the following:

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. http://doi.org/10.1371/journal.pone.0220457

Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. https://doi.org/10.5281/zenodo.2572018

OVERVIEW OF CONTENTS

This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:

• Raw DEM and Soils data
  • Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the National Elevation Dataset)
    • DEM_Individual_Tiles: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.
    • DEMs_Merged: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM.
  • Soils Data (Map services and data available from Natural Resources Conservation Service Web Soil Survey, U.S. Department of Agriculture)
    • Animas-Dolores_Area_Soils: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).
    • Cortez_Area_Soils: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).
• ArcGIS Map Packages
  • Goodman_Watershed_Full_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).
  • Goodman_Watershed_Mesa-Only_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.
  • Crow_Canyon_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).
  • Coffey_Watershed_SMPM_Analysis: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).

For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."

LICENSES

Code: MIT year: 2019
Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton

CONTACT

Andrew Gillreath-Brown, PhD Candidate, RPA
Department of Anthropology, Washington State University
andrew.brown1234@gmail.com – Email
andrewgillreathbrown.wordpress.com – Web

PurposeThis job aid will lead the GIS analyst through the process of manually creating an incident map journal and how to create additional pages for the journal. This process should be used at the beginning of an incident and then the journal should be maintained to assure it remains viable. The incident map journal serves as a curated center to place maps, apps, and dashboards relevant to the incident.

This job aid assumes a working knowledge of how to create maps, apps, and dashboards on ArcGIS Online. For a tutorial, go to the Create apps from maps - ArcGIS Tutorial.Example workflow for the Geo-Enabled Plans Session at InSPIRE. Job Aid developed by FEMA GIS to enable GIS analysts to rapidly spin-up a standardized incident journal.

The paper and journal lists used in the study.

The dataset, GeoGPT-Data, is the experimental data used in the paper "GeoGPT: An Assistant for Understanding and Processing Geospatial Tasks", which has been accepted by the International Journal of Applied Earth Observations and Geoinformation (JAG). If you need to use this dataset, please cite our paper.

Read on page 9 of the Leaving School magazine how Sam Keast (Senior GIS Analyst at the Ministry of Social Development) benefited from studying Geography.

IntroductionGeographic Information Systems (GIS) and spatial analysis are emerging tools for global health, but it is unclear to what extent they have been applied to HIV research in Africa. To help inform researchers and program implementers, this scoping review documents the range and depth of published HIV-related GIS and spatial analysis research studies conducted in Africa.MethodsA systematic literature search for articles related to GIS and spatial analysis was conducted through PubMed, EMBASE, and Web of Science databases. Using pre-specified inclusion criteria, articles were screened and key data were abstracted. Grounded, inductive analysis was conducted to organize studies into meaningful thematic areas.Results and discussionThe search returned 773 unique articles, of which 65 were included in the final review. 15 different countries were represented. Over half of the included studies were published after 2014. Articles were categorized into the following non-mutually exclusive themes: (a) HIV geography, (b) HIV risk factors, and (c) HIV service implementation. Studies demonstrated a broad range of GIS and spatial analysis applications including characterizing geographic distribution of HIV, evaluating risk factors for HIV, and assessing and improving access to HIV care services.ConclusionsGIS and spatial analysis have been widely applied to HIV-related research in Africa. The current literature reveals a diversity of themes and methodologies and a relatively young, but rapidly growing, evidence base.

The purpose of this study was to: 1) examine the effect of Arizona Highways Magazine (AHM) on tourism,2) determine trip characteristics of AHM subscribers traveling in Arizona, and 3) calculate a benefit/cost ratio for AHM based on the magazine’s cost and revenues as well as the value-added economic impact. Findings suggest that:  AHM subscribers are demographically similar to other people with an interest in Arizona as a travel destination with the exception of age and related variables: AHM subscribers are older.  A very high percentage of AHM subscribers have taken trips in Arizona over the past five years, with many visiting multiple times. Most out-of-state subscribers stay for one to two weeks when they visit, most often traveling with a spouse or partner.  Subscribers use AHM fairly extensively as a source of travel information.  About 29 percent of out-of-state subscribers who visited in the past five years indicated that AHM influenced them to visit Arizona on their most recent trip.  In addition to its influence on visitors’ decision to select Arizona as a destination, the magazine especially influenced decisions related to specific destinations or attractions and choices regarding travel routes.  Subscribers have spent an average of over $195.2 million on travel annually over the past five years, and $41.5 million of those expenditures can be directly attributed to AHM and its influence on the travel behavior of out-of-state subscribers. This amounts to a benefit/cost ratio 7.8 to 1 at the very least.

FEMA's Hurricane Incident Journal provides relevant spatial decision-making support for FEMA leadership and a view into federal information available to the general public. This website is a part of the FEMA GeoPlatform.

Individual applications shown in this journal are linked to at the bottom of each section.

Modeled damage assessments are based on flood depth grids, then verified with satellite imagery. Depth grids can be Observed (data from river, coastal, tide gauges), or Forecasted (created from Advanced Hydrologic Prediction Service, AHPS, forecasts). Remote Sensing contains flood extents and other data from NASA and Copernicus. Surge Inundation Dashboard

Analysis will update when there is greater than 10% chance of 5' or more of Surge Inundation.

Hurricane Force Winds Dashboard

Analysis will update automatically when there is a greater than 50% chance of hurricane force winds (64 knot, 74 mph) over land. Wind data is taken from the latest NOAA advisory.

To better understand factors potentially contributing to the occurrence of rainfall-induced landslides in Puerto Rico, we evaluated the locations of landslides there following Hurricane Maria (Hughes et al., 2019) and potential contributing factors. This data release provides results of evaluations of landslide locations compared to soil classification and land cover, which involved frequency-ratio analyses (for example, Lee and Pradhan, 2006; Lee et al., 2007; He and Beighley, 2008; Lepore et al., 2012; Chalkias et al., 2014). Soil classification data were obtained from the U.S. Department of Agriculture Natural Resources Conservation Service (2018) and land cover data were obtained from the Puerto Rico Gap Analysis Program (Gould et al., 2008). The data presented herewith were produced during a study described in Hughes, K.S., and Schulz, W.H., ####, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-file Report #####. Three files are included with this data release. Data files soil_classification_results.csv and land_cover_results.csv provide results of the analyses of landslide locations compared to soil classification and land cover, respectively. A read-me file (readme.txt) provides the information contained in this summary and additional description of data available from the data files. References Chalkias, C., Kalogirou, S., and Ferntinou, M., 2014, Landslide susceptibility, Peloponnese Peninsula in South Greece: Journal of Maps, v. 10, no. 2, p. 211-222. Gould, W.A., Alarcón, C., Fevold, B., Jiménez, M.E., Martinuzzi, S., Potts, G., Quiñones, M., Solórzano, M., and Ventosa, E., 2008, The Puerto Rico Gap Analysis Project. Volume 1: Land cover, vertebrate species distributions, and land stewardship. Gen. Tech. Rep. IITF-GTR-39. Río Piedras, PR: U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry. 165 p. https://www.sciencebase.gov/catalog/item/560c3b2de4b058f706e5411e. Last accessed 12 September 2019. He, Y., and Beighley, R.E., 2008, GIS‐based regional landslide susceptibility mapping: a case study in southern California: Earth Surface Processes and Landforms, v. 33, no. 3, p. 380-393. Hughes, K.S., Bayouth García, D., Martínez Milian, G.O., Schulz, W.H., and Baum, R.L., 2019, Map of slope-failure locations in Puerto Rico after Hurricane María: U.S. Geological Survey data release: https://doi.org/10.5066/P9BVMD74. https://www.sciencebase.gov/catalog/item/5d4c8b26e4b01d82ce8dfeb0. Last accessed 12 September 2019. Lee, S., and Pradhan, B., 2006, Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia: Journal of Earth System Science, v. 115, no. 6, p. 661-672. Lee, S., Ryu, J-H., and Kim, I-S., 2007, Landslide susceptibility analysis and its verification using likelihood ratio, logistic regression, and artificial neural network models: case study of Youngin, Korea: Landslides v. 4, p. 327–338. Lepore, C., Kamal, S.A., Shanahan, P., and Bras, R.L., 2012, Rainfall-induced landslide susceptibility zonation of Puerto Rico: Environmental Earth Sciences, v. 66, p. 1667-1681. U.S. Department of Agriculture Natural Resources Conservation Service, 2018, Soil Survey Geographic (SSURGO) database for Puerto Rico, all regions: https://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx. Last accessed 12 September 2019.

The Figure files contain GIS files used to derive the maps of Michigan. The Tables files are .dbf files that provide values reflecting changes over time and differences in approach for grouped census tracts.

FEMA's Flooding Incident Journal provides relevant spatial decision-making support for FEMA leadership and a view into federal information available to the general public. This website is a part of the FEMA GeoPlatform.Individual applications shown in this journal are linked to at the bottom of each section.Any modeled damage assessments are based on flood depth grids, then verified with satellite imagery. Depth grids can be Observed (data from river, coastal, tide gauges), or Forecasted (created from Advanced Hydrologic Prediction Service, AHPS, forecasts). Remote Sensing contains flood extents and other data from NASA and Copernicus.

In order to protect the biological diversity of marine life in Australia's Exclusive Economic Zone (EEZ), the commonwealth government has passed the Environmental Protection and Biodiversity Conservation (EPBC) Act. The Act is being implemented through preparation of regional marine plans (commenced in 2001) and by designing networks of representative marine protected areas (MPAs) in both commonwealth and state waters. In the absence of direct information about the distribution of seabed biodiversity, appropriate surrogates must be used instead. A major constraint is the short time-frame available to managers to make decisions; only information that is readily accessible and available can be used under these circumstances. Existing seabed bathymetry data were used to produce a geomorphic features map of the Australian EEZ. This map was used in conjunction with existing fish diversity information and other data to derive a Benthic Bioregionalisation (2005) that subdivides Australia's EEZ into 41 bioregions including 24 biologically unique provinces. Biophysical variables measured at broad spatial scales apart from bathymetry (and derived variables such as seabed slope) include ocean primary production, seabed sediment properties, temperature and sediment mobilisation due to waves and tides. To better characterise habitats on the Australian continental margin, Geoscience Australia has created 'seascape' maps that integrate multiple layers of spatial data that are useful for the prediction of the distribution of biodiversity.

Existing seabed bathymetry data were used to produce a geomorphic features map of the Australian EEZ. This map was used in conjunction with existing fish diversity information and other data to derive a Benthic Bioregionalisation (2005) that subdivides Australia's EEZ into 41 bioregions including 24 biologically unique provinces.

This paper uses text data mining to identify long-term developments in tourism academic research from the perspectives of thematic focus, geography, and gender of tourism authorship. Abstracts of papers published in the period of 1970–2017 in high-ranking tourist journals were extracted from the Scopus database and served as data source for the analysis. Fourteen subject areas were identified using the Latent Dirichlet Allocation (LDA) text mining approach. LDA integrated with GIS information allowed to obtain geography distribution and trends of scholarly output, while probabilistic methods of gender identification based on social network data mining were used to track gender dynamics with sufficient confidence. The findings indicate that, while all 14 topics have been prominent from the inception of tourism studies to the present day, the geography of scholarship has notably expanded and the share of female authorship has increased through time and currently almost equals that of male authorship.

This data support the paper "A systematic review on the integration of remote sensing and GIS to forest and grassland ecosystem health attributes, indicators, and measures " by Irini Soubry, Thuy Doan, Thuan Chu and Xulin Guo 2021 in the journal of "Remote Sensing" by MDPI. It includes the "Search_Effort.csv" list with the keywords and number of studies selected for further examination, the "Potential_Studies.csv" with the post-filtering of suitability and notes related to each study, the "Metadata.csv" with the information collected for each metadata variable per study, and the "ExtractedData.csv" with the information collected for each extracted dta variable per study. More information about the data collection and procedures can be found in the respective manuscript.

DATASET: Alpha version 2000 and 2010 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and MODIS-derived urban extent change built in. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described on the website and in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM00urbchg.tif = Vietnam (VNM) population count map for 2000 (00) adjusted to match UN national estimates and incorporating urban extent and urban population estimates for 2000. DATE OF PRODUCTION: July 2013 Dataset construction details and input data are provided here: www.asiapop.org and here: http://www.plosone.org/article/info:doi/10.1371/journal.pone.0055882

This report of the work undertaken by the Energy Infrastructure and Modeling and Analysis Division ( EIMA ) of the U.S. Department of Energy Office of Electricity Delivery and Energy Reliability (OE) assesses the potential sea level rise and storm surge risks to energy assets in the Metropolitan Statistical Area (MSA) of specific cities in the United States. Here's the DOE article about the report which also links to the story map: https://energy.gov/oe/articles/visualizing-energy-infrastructure-exposure-storm-surge-and-sea-level-rise

For author information and the view count for this story map, please see the entry for it: https://www.arcgis.com/home/item.html?id=58f90c5a5b5f4f94aaff93211c45e4ec

This story map was created by ICF International ( Contact Kevin Wright ): http://www.icfi.com/services/it-solutions/geospatial-solutions-gis

The air quality in Beijing, especially its PM2.5 level, has become of increasing public concern because of its importance and sensitivity related to health risks. A set of monitored PM2.5 data from 31 stations, released for the first time by the Beijing Environmental Protection Bureau, covering 37 days during autumn 2012, was processed using spatial interpolation and overlay analysis. Following analyses of these data, a distribution map of cumulative exceedance days of PM2.5 and a temporal variation map of PM2.5 for Beijing have been drawn. Computational and analytical results show periodic and directional trends of PM2.5 spreading and congregating in space, which reveals the regulation of PM2.5 overexposure on a discontinuous medium-term scale. With regard to the cumulative effect of PM2.5 on the human body, the harm from lower intensity overexposure in the medium term, and higher overexposure in the short term, are both obvious. Therefore, data of population distribution were integrated into the aforementioned PM2.5 spatial spectrum map. A spatial statistical analysis revealed the patterns of PM2.5 gross exposure and exposure probability of residents in the Beijing urban area. The methods and conclusions of this research reveal relationships between long-term overexposure to PM2.5 and people living in high-exposure areas of Beijing, during the autumn of 2012.

For complete collection of data and models, see https://doi.org/10.21942/uva.c.5290546.Supplemental material for: Hierarchical geomorphological mapping in mountainous areas, Matheus G.G. De Jong, Henk Pieter Sterk, Stacy Shinneman & Arie C. Seijmonsbergen. Submitted to Journal of Maps in 2020, revisions made in 2021.These layer files will produce the complete geomorphological legend, even when all geomorphological units are not present in the dataset. When visualizing results, we recommend the following optimal scale ranges: 1:2,500 - 1:10,000 for Tier 3, 1:10,001 to 1:30,000 for Tier 2 and ≥ 1:30,001 for Tier 1.The complete set of layer files ("Geomorphological Map Vorarlberg - Tier 1", "Geomorphological Map Vorarlberg - Tier 2" and "Geomorphological Map Vorarlberg - Tier 3") are intended to visualize output of a model that creates tiers (columns) of geomorphological features (Tier 1, Tier 2 and Tier 3) in the landscape of Vorarlberg, Austria, each with an increasing level of detail.

Description: The dataset contains all publicly accessible green spaces in the city of Dresden, including an attribute table with 38 different indicators. The green spaces and indicator values ​​are the central data basis for evaluating the green spaces according to criteria or suitability for certain activities using the meinGrün app (app.meingruen.org). The green space polygons were generated using an automatic approach described in Ludwig et al. (2021) is described in more detail. The formation is based on assumptions about physical barriers, specifically the road, rail and water network and boundaries of certain adjacent land use class combinations. For Dresden, the green area polygons were formed by a combined processing of OpenStreetmap and urban data, especially a geometry of statistical blocks, the parks and green areas, playgrounds, cemeteries, allotments and forests. Indicators were processed by the Leibniz Institute for Ecological Spatial Development, the Heidelberg Institute for Geoinformation Technology at the University of Heidelberg and the Institute for Cartography at the TU Dresden. The data basis and calculation rules used to calculate the indicators are documented in the metadata description. # References: Cakir, S.; Hecht, R.; Krellenberg, K. (2021): Sensitivity analysis in multi-criteria evaluation of the suitability of urban green spaces for recreational activities. In: AGILE GIScience Series, 2, 22 (2021) https://doi.org/10.5194/agile-giss-2-22-2021 Hecht, R.; Artmann, M.; Brzoska, P. et al. (2021): A web app to generate and disseminate new knowledge on urban green space qualities and their accessibility. ISPRS Annals (accepted) Krellenberg, K.; Artmann, M.; Stanley, C.; Hecht, R. (2021): What to do in, and what to expect from, urban green spaces - Indicator-based approach to assess cultural ecosystem services. In: Urban Forestry & Urban Greening (2021) 59: 126986 https://doi.org/10.1016/j.ufug.2021.126986 Krellenberg, K.; Hecht, R. (2021): Generate new knowledge about urban greenery with a mobile app. In: GIS.business - the magazine for geoinformation (2021) 3/2021, p.41-43 https://doi.org/10.21241/ssoar.73701 Ludwig, C.; Hecht, R.; Lautenbach, S.; Schorcht, M.; Zipf, A. (2021): Mapping Public Urban Green Spaces Based on OpenStreetMap and Sentinel-2 Imagery Using Belief Functions. In: ISPRS International Journal of Geo-Information 10 (2021) 4, p.251 https://doi.org/10.3390/ijgi10040251