Lab 4 GIST 603A Introductin to ArcGIS Online University of Arizona MS GIST programLab 4 – ArcGIS OnlineArcGIS Online is a simple cloud-based utility for producing, editing, and sharing geospatial data. Designed by ESRI, the makers of the popular ArcGIS software suite, ArcGIS Online is meant to act as a Web-based mapping solution for everyone from GIS professionals to those with no formal GIS training.ArcGIS Online allows you to:Upload and manipulate dataMap points, lines and areasCreate point, cloropleth, and other thematic mapsEmbed maps in Web sitesShare maps in a multitude of waysView maps on mobile devices

In Module 2 Lesson 1, we will take a deeper dive into Geographic Information Systems (GIS) technology. We'll explore different types of GIS data, the importance of data attributes and queries, data symbolization, and ways to access GIS technology.

This metadata record describes the acquisition and production of 1 foot contours for 5 coastal counties Hancock, Harrison, Jackson, Pearl River and Stone. The breaklines were collected from digital imagery with a 15 cmground sample distance (GSD) for the project area for the 1 foot contour area and 30 cm for the 5 foot contour area. All imagery was acquired in spring 2007 and processed during the spring & summer of 2007. The imagery is from a project tasked by Mississippi Geographic Information, LLC (MGI) with Work Orders ED-9 & ED-9A. EarthData International, Inc. was authorized to undertake this project in accordance with the terms and conditions of the professional service agreement between MGI and EarthData International, Inc., dated February 14, 2007.

The Kansas Master Ground-water Well Inventory (MWI) is a central repository that imports and links together the State's primary ground-water well data sets- KDHE's WWC5, KDA-DWR's WIMAS, and KGS' WIZARD into a single, online source. The most "accurate" of the common source fields are used to represent the well sites, for example- GPS coordinates if available are used over other methods to locate a well. The MWI maintains the primary identification tags to allow specific well records to be linked back to the original data sources.This data is compiled by the Kansas Geological Survey. For more information, please see the Groundwater Master Well Inventory page.

Updated Continually

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).

GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.

GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.

GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

fid - Unique identifier

bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.

bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.

gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).

bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).

prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).

cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.

doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.

heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.

feat_code - Code describing the type of building, pulled from the Building Footprint Data.

groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.

qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'

notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.

classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)

digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)

newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)

original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.

address - Address based on MapPLUTO, joined to the dataset based on bbl.

borough - Borough abbreviation pulled from MapPLUTO.

ownertype - Owner type field pulled from MapPLUTO.

zonedist1 - Zoning District 1 type pulled from MapPLUTO.

spdist1 - Special District 1 pulled from MapPLUTO.

bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

xcoord - Longitude in decimal degrees.

ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

Access the file geodatabase source data in SC State Plane coordinate system

See 2018 IPTP report for more detailed methodology results discussions. The IPTP analysis parameters and spatial units were determined by available data and expert opinion. An advisory panel comprised of local, state, and federal natural resource managers and invasive plant experts suggested a number of managerial and environmental measures to be considered in the analysis. The expert panel included professionals from Arizona Department of Agriculture (ADA), Arizona Department of Transportation (ADOT), Arizona Game and Fish Department (AGFD), Arizona State Land Department (ASLD), Arizona-Sonora Desert Museum, Bureau of Land Management (BLM), DFFM, U.S. Fish and Wildlife Service (US FWS), University of Arizona Cooperative Extension, USDA FS. The initial list of general topics included: fire risk, riparian areas, protected species, spread corridors, Invasive Plant Threat Levels, areas of prior treatments, economic impact, accessibility for treatment, higher risk to introduction (Wildland Urban Interface - WUI), water bodies of high value, undeveloped areas, and sustainability. Existing local, state-, and nation-wide datasets were a good fit for most topics; but, in a few cases, alternate datasets had to be created or found. Economic impact, accessibility for treatments, water bodies of high value, and sustainability parameters were not included because of quantification difficulties, due to limited data availability, and to reduce double counting. We attempted to capture a measure of invasive plants treatment sustainability by supporting areas with active, local weed or invasive plant management groups but were not able to capture it spatially in a representative manner at the time of analysis.For better cross comparison, the selected dataset's scores were converted into a normalized index with a value range between 0 (“cool”) and 1 (“hot”):normalized index value = (score value – score min) / (score max – score min)All indices have been calculated for a 1 square mile hexagon analysis area. A hexagon GIS layer provided by AGFD ensured a standardized spatial unit at an appropriate resolution which was unmistakable from land ownership boundaries.The 8 normalized indices were averaged by adding them together and dividing them by 8 to generate a final score. Besides normalizing all scores between 0 and 1, we did not apply any statistical corrections or preference weights to the 8 sub-indices.

Zoning designations of land use within the unincorporated areas of Napa County.

The Coconino Plateau Water Demand Web Application is an interactive tool that illustrates water demand within the Coconino Plateau. I created it as a tool for the CPWP and other stakeholders to use to find water demand (in acre-ft/year), surface water sources if applicable, and water use information (municipal, effluent, industrial, or agricultural) for a specific town or place of interest within the Coconino Plateau.Other Information:The dashboard is property of the Coconino Plateau Watershed Partnership and uses data from the ADWR Community Water System Database. I completed this as my capstone project to fulfill the requirements of the masters in GIS with Penn State.

This conflated baseline consist of two Digital Shoreline Analysis System (DSAS) Process runs.The original In 2000, the Maryland Geological Survey (MGS) was awarded a Coastal Zone Management grant to complete the acquisition of a recent (ca. 1990) digital shoreline for the coastal regions of Maryland -- the Chesapeake Bay, its tributaries, the coastal bays, and the Atlantic coast. MGS contracted the services of EarthData International, Inc. (EDI), currently of Frederick, Md., to extract shorelines from an existing wetlands delineation, which was based on photo interpretation of 3.75-minute digital orthophoto quarter quads (DOQQs). The 2000 baseline which were not created seaward includes all Maryland shoreline areas except for Anne Arundel, Baltimore, Calvert, Harford and Prince George's counties currently. The newest (2015) updated baselines were created offshore (seaward) of the shorelines utilized in DSAS analysis. The baselines were created by 1) buffering at a distance of 10m around the master shoreline feature class converting the buffer polygon to a line, and erasing the landward portion of the buffer line; and 2)manually digitizing baselines up the centerline of tributaries/rivers and other areas where baselines were needed but the buffer-created baselines did not reach. Funding for this data set was provided by two Projects of Special Merit (CZM # 14-14-1868 CZM 143 and CZM # 14-15-2005 CZM 143), funded by the National Oceanic and Atmospheric Administration (NOAA) and made available to MGS through the Department of Natural Resources (MD DNR) Chesapeake and Coastal Service (CCS). MGS wishes to thank the following project partners: 1) MD DNR CCS, Contact: Mr. Chris Cortina, Role: CCS Project Manager; 2) NOAA, Contact: Mr. Doug Graham, NOAA National Geodetic Survey, Role: Project partner & source of historical and recent shorelines; 3) MD DNR Critical Areas Commission (CAC), Contact: Ms. Lisa Hoerger, Role: Project partner & source of recent shorelines; 4) Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University, Contact: Ryan Mello, Role: Performing the critical area re-mapping for MD DNR CAC and supplying MGS with CAC shorelines; and 5) Ms. Lamere Hennesse, MGS Geologist, retired, Role: Project guidance & technical support.Previous, original Maryland Baselines, credit go to MGS, working collaboratively with Towson University’s Center for Geographic Sciences (CGIS), subsequently used the recent shorelines, along with historical ones, as input into a U.S. Geological Survey (USGS) program, the Digital Shoreline Analysis System (DSAS) (Danforth and Thieler, 1992; Thieler and others, 2001). DSAS determines linear rates of shoreline change (erosion or accretion) along closely spaced, shore-normal transects. This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Map Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Hydrology/MD_ShorelineChanges/MapServer/1