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

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.

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.

Wildfire Incident Journal currently in testing phase.

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.

Tagged image tiles as well as the Faster-RCNN framework for automatic extraction of road intersection points from USGS historical maps of the United States of America. The data and code have been prepared for the paper entitled "Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks" submitted to "International Journal of Geographic Information Science". The image tiles have been tagged manually. The Faster RCNN framework (see https://arxiv.org/abs/1611.10012) was captured from:https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

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.

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.

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

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

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.

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 project developed data, information and knowledge on the spatial distribution of poverty in eight developing countries. The eight case studies included poverty and food security maps, the data sets, preprints of journal articles for a special issue of Food Policy, standardized geospatial metadata and a browse graphic showing key maps. The different case studies use cutting-edge poverty mapping techniques such as small area estimation. The countries included in the project were Bangladesh, Ecuador, Kenya, Malawi, Mexico, Nigeria, Sri Lanka and Vietnam. The data set also includes data for Honduras. The case studies were published here: https://www.sciencedirect.com/journal/food-policy/vol/30/issue/5

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.

A zipped folder containing the data files that archive the answers to the Arizona Highways Magazine 2019 surveys.

This paper was presented at SGEG Conference, Canberra, January 1997

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

Lorient Agglomeration maintains a database in the Geographic Information System (GIS) describing the public space in its organisation and its uses (location of schools, public facilities, ports, access to the sea, outline of the natural areas managed by Lorient Aglomeration, production of PLUs, bathymetric repositories, addresses, aerial view of the territory, old aerial views, accessibility of equipment, bathing areas, emergency stations, location of waste sites, activity areas, etc.). This GIS database, which is a common good, needs to be sustained and enriched in order to further improve knowledge of the territory. There are many collection points where you deposit papers (newspapers, magazines, magazines, magazines, mail and envelopes), glass and clothing. This database will allow you to geolocate them. Collection context Data processing in collaboration with the Waste Life Framework Directorate of Lorient Agglomeration. On-site tracking during tours. Collection method Identifying collection points from aerial photography 20 cm 2016 Attributes | Champ | Alias | Type | | — | — | | — | | ‘id_providen’ | ‘int4’ | | ‘textile’ | ‘int4’ | | ‘id_pav’ | ‘serial,PrimaryKey’ | | ‘glass’ | ‘int4’ | | ‘paper’ | ‘int4’ | | ‘id_troncon’ | ‘int4’ | ‘id_troncon’ | ‘id_volume’ | ‘int4’ | | ‘nom_rue’ | ‘varchar’ | | ‘name_rue_terrain’ | ‘varchar’ | | ‘acc_glass’ | ‘int4’ | | ‘acc_paper’ | ‘int4’ | ‘int4’ | | ‘acc_textile’ | ‘int4’ | | ‘path’ | ‘varchar’ | | ‘winscp’ | ‘varchar’ | | ‘name’ | ‘varchar’ | | ‘extension’ | ‘varchar’ | | ‘camera’ | ‘varchar’ | | ‘date’ | ‘varchar’ | | ‘date_hour’ | ‘varchar’ | | ‘lat_deci’ | ‘varchar’ | | ‘lon_deci’ | ‘varchar’ | | ‘lat_dms’ | ‘varchar’ | | ‘latituderef’ | ‘varchar’ | | ‘lon_dms’ | ‘varchar’ | ‘lon_dms’ | | ‘longituderef’ | ‘varchar’ | | ‘orientation’ | ‘varchar’ | | ‘pixelxdimension’ | ‘varchar’ | | ‘pixelydimension’ | ‘varchar’ | | ‘date_crea’ | ‘date’ | ‘date_crea’ | | ‘date_modif’ | ‘date’ | ‘date’ | For more information, see the metadata on the Isogeo catalog.