This archive contains all spatial data from the 2015 Interactive Forest Atlas of Cameroon.

The Unpublished Digital Post-Hurricane Sandy Geomorphological-GIS Map of the Gateway National Recreation Area: Sandy Hook, Jamaica Bay and Staten Island Units, New Jersey and New York is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (gate_geomorphology.gdb), a 10.1 ArcMap (.MXD) map document (gate_geomorphology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (gate_geomorphology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (gate_gis_readme.pdf). Please read the gate_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Rutgers University Institute of Marine and Coastal Sciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (gate_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/gate/gate_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:6,000 and United States National Map Accuracy Standards features are within (horizontally) 5.08 meters or 16.67 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 18N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Gateway National Recreation Area.

Bear Lake provides a unique location to use bathymetric data to analyze the relationship between changing water surface elevations and the accessible spawning habitat for fish species. The spawning habitat for the prey species of Bear Lake consists of cobble which is present in the littoral zone of the lake. The littoral zone is classified as the area of the water column that has light penetration, sufficient for macrophytes to photosynthesis, to reach the sediment floor of the lake. The analysis was performed using ESRI’s ArcMap and Python coding to calculate, automate, and illustrate this relationship; and to provide a possible methodology for water and wildlife management to apply to their unique situations to make informed decisions in the future. This method is advantageous when analyzing present or future conditions because of its versatility to create hypothetical scenarios.

This statistic shows the percentage of agricultural operations in Canada using GIS mapping technology in 2015, by farm size. In that year, 52.7 percent of Canadian farms with 10,000 or more acres of land reported using GIS mapping.

Term project for Utah State University CEE 6440 GIS for Water Resources

This resource is a proposal for a project for the USU CEE 6440, GIS in Water Resources class. The project deals with the use of GIS mapping and hydrologic data for use in outdoor recreation.

Data of high resolution (10kmx10km) Global Horizontal Irradiance (GHI) for Ghana for the years 2000, 2001 and 2002. The data are available for monthly and annual sums stored in a ESRI-Shapefile. The data are helpful for the assessment of the solar potential of the country and can give project developer a first impression of the solar resource of the country. Citation: DLR & Negawatt challenge.

This generic resource illustrates to students in the CEE6400 GIS in Water Resources Class at Utah State University how to prepare HydroShare resources to post term projects.

This is a generic information for Hydroshare for the student Ryan James for the class of CEE 6440 GIS spring 2015.

This generic resource illustrates the term project proposal of Carl Mackley, student in CEE6440, GIS in Water Resources class at Utah State University.

Download UrbanTreeCanopy_2015.zip. The following information was produced from the 2015 Urban Tree Canopy Assessment for the City of Louisville, KY sponsored by the Office of Sustainability. It is based on 2012 LOJIC Base Map data. It includes shapefiles and raster feature classes in a file geodatabase. The study was performed by Davey Resource Group.

The purpose of this project is to show how the health of fishes can be impacted by high or low levels of dissolved oxygen in river systems. The oxygen concentrations will be compared with theoretical values to determine if the river system is clean or polluted. A polluted system or system that has sufficient nutrients including phosphorus has an explosion plant growth and when this plants die the bacteria feed on the organic matter, in the process consume an increased amount of DO. This why a clean water system is ideal for a healthy aquatic system (McLean, 2015).

[Metadata] - 2015 Zip Code Tabulation Areas (ZCTA) with population figures from American Community Survey 5-year estimates. Source: U.S. Census Bureau, 2016. The American Community Survey (ACS) is an ongoing survey that provides data every year ... the 5-year estimates from the ACS are "period" estimates that represent data collected over a period of time, from 2011 to 2015. For more information about the ACS, please visit https://www.census.gov/programs-surveys/acs/.For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/zcta15.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

This term project will use data collected by the EPA to show a list of water treatment facilities across the United States, what they use to treat their water and a risk assessment of how much chromium contamination could be possible from their water resources used in drinking water treatment.

The Unpublished Digital Post-Hurricane Sandy Geomorphological-GIS Map of the Staten Island Unit, Gateway National Recreation Area, New York is composed of GIS data layers and GIS tables in a 10.1 file geodatabase (stis_geomorphology.gdb), a 10.1 ArcMap (.MXD) map document (stis_geomorphology.mxd), individual 10.1 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (gate_geomorphology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (gate_gis_readme.pdf). Please read the gate_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.1 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Rutgers University Institute of Marine and Coastal Sciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (stis_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/gate/stis_post-sandy_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:6,000 and United States National Map Accuracy Standards features are within (horizontally) 5.08 meters or 16.67 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 18N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Gateway National Recreation Area.

The GIS-based Time model of Gothenburg aims to map the process of urban development in Gothenburg since 1960 and in particular to document the changes in the spatial form of the city - streets, buildings and plots - through time. Major steps have in recent decades been taken when it comes to understanding how cities work. Essential is the change from understanding cities as locations to understanding them as flows (Batty 2013)1. In principle this means that we need to understand locations (or places) as defined by flows (or different forms of traffic), rather than locations only served by flows. This implies that we need to understand the built form and spatial structure of cities as a system, that by shaping flows creates a series of places with very specific relations to all other places in the city, which also give them very specific performative potentials. It also implies the rather fascinating notion that what happens in one place is dependent on its relation to all other places (Hillier 1996)2. Hence, to understand the individual place, we need a model of the city as a whole.

Extensive research in this direction has taken place in recent years, that has also spilled over to urban design practice, not least in Sweden, where the idea that to understand the part you need to understand the whole is starting to be established. With the GIS-based Time model for Gothenburg that we present here, we address the next challenge. Place is not only something defined by its spatial relation to all other places in its system, but also by its history, or its evolution over time. Since the built form of the city changes over time, often by cities growing but at times also by cities shrinking, the spatial relation between places changes over time. If cities tend to grow, and most often by extending their periphery, it means that most places get a more central location over time. If this is a general tendency, it does not mean that all places increase their centrality to an equal degree. Depending on the structure of the individual city’s spatial form, different places become more centrally located to different degrees as well as their relative distance to other places changes to different degrees. The even more fascinating notion then becomes apparent; places move over time! To capture, study and understand this, we need a "time model".

The GIS-based time model of Gothenburg consists of: • 12 GIS-layers of the street network, from 1960 to 2015, in 5-year intervals • 12 GIS-layers of the buildings from 1960 to 2015, in 5-year intervals - Please note that this dataset has been moved to a separate catalog post (https://doi.org/10.5878/t8s9-6y15) and unpublished due to licensing restrictions on its source dataset. • 12 GIS- layers of the plots from1960 to 2015, in 5-year intervals

In the GIS-based Time model, for every time-frame, the combination of the three fundamental components of spatial form, that is streets, plots and buildings, provides a consistent description of the built environment at that particular time. The evolution of three components can be studied individually, where one could for example analyze the changing patterns of street centrality over time by focusing on the street network; or, the densification processes by focusing on the buildings; or, the expansion of the city by way of occupying more buildable land, by focusing on plots. The combined snapshots of street centrality, density and land division can provide insightful observations about the spatial form of the city at each time-frame; for example, the patterns of spatial segregation, the distribution of urban density or the patterns of sprawl. The observation of how the interrelated layers of spatial form together evolved and transformed through time can provide a more complete image of the patterns of urban growth in the city.

The Time model was created following the principles of the model of spatial form of the city, as developed by the Spatial Morphology Group (SMoG) at Chalmers University of Technology, within the three-year research project ‘International Spatial Morphology Lab (SMoL)’.

The project is funded by Älvstranden Utveckling AB in the framework of a larger cooperation project called Fusion Point Gothenburg. The data is shared via SND to create a research infrastructure that is open to new study initiatives.

1. Batty, M. (2013), The New Science of Cities, Cambridge: MIT Press.
2. Hillier, B., (1996), Space Is the Machine. Cambridge: University of Cambridge

12 GIS-layers of the street network in Gothenburg, from 1960 to 2015, in 5-year intervals. File format: shapefile (.shp), MapinfoTAB (.TAB). The coordinate system used is SWEREF 99TM, EPSG:3006.

See the attached Technical Documentation for the description and further details on the production of the datasets. See the attached Report for the description of the related research project.

Historical holdings data showing quarterly positions, market values, shares held, and portfolio percentages for GIS held by HUNTER ASSOCIATES INVESTMENT MANAGEMENT LLC from Q4 2015 to Q1 2025

The purpose of this project is to map wetland areas near the Great Salt Lake and display the changes that these areas have seen during drought conditions.

A global analysis of accessibility to high-density urban centres at a resolution of 1×1 kilometre for 2015, as measured by travel time.

To model the time required for individuals to reach their most accessible city, we first quantified the speed at which humans move through the landscape. The principle underlying this work was that all areas on Earth, represented as pixels within a 2D grid, had a cost (that is, time) associated with moving through them that we quantified as a movement speed within a cost or ‘friction’ surface. We then applied a least-cost-path algorithm to the friction surface in relation to a set of high-density urban points. The algorithm calculated pixel-level travel times for the optimal path between each pixel and its nearest city (that is, with the shortest journey time). From this work we ultimately produced two products: (a) an accessibility map showing travel time to urban centres, as cities are proxies for access to many goods and services that affect human wellbeing; and (b) a friction surface that underpins the accessibility map and enables the creation of custom accessibility maps from other point datasets of interest. The map products are in GeoTIFF format in EPSG:4326 (WGS84) project with a spatial resolution of 30 arcsecs. The accessibility map pixel values represent travel time in minutes. The friction surface map pixels represent the time, in minutes required to travel one metre. This DANS data record contains these two map products.

This dataset is a compilation of tax parcel polygon and point layers from the seven Twin Cities, Minnesota metropolitan area counties of Anoka, Carver, Dakota, Hennepin, Ramsey, Scott and Washington. The seven counties were assembled into a common coordinate system. No attempt has been made to edgematch or rubbersheet between counties. A standard set of attribute fields is included for each county. (See section 5 of the metadata). The attributes are the same for the polygon and points layers. Not all attributes are populated for all counties.

The polygon layer contains one record for each real estate/tax parcel polygon within each county's parcel dataset. Some counties have polygons for each individual condominium, and others do not. (See Completeness in Section 2 of the metadata for more information.) The points layer includes the same attribute fields as the polygon dataset. The points are intended to provide information in situations where multiple tax parcels are represented by a single polygon. The primary example of this is the condominium, though some counties stacked polygons for condos. Condominiums, by definition, are legally owned as individual, taxed real estate units. Records for condominiums may not show up in the polygon dataset. The points for the point dataset often will be randomly placed or stacked within the parcel polygon with which they are associated.

The polygon layer is broken into individual county shape files. The points layer is provided as both individual county files and as one file for the entire metro area.

In many places a one-to-one relationship does not exist between these parcel polygons or points and the actual buildings or occupancy units that lie within them. There may be many buildings on one parcel and there may be many occupancy units (e.g. apartments, stores or offices) within each building. Additionally, no information exists within this dataset about residents of parcels. Parcel owner and taxpayer information exists for many, but not all counties.

Polygon and point counts for each county are as follows (Updated annually, current as of 1/08/2016):

polygons / points
Anoka - 131121 / 131121
Carver - 40510 / 40509
Dakota - 140770 / 154109
Hennepin - 429241 / 429241
Ramsey - 154512 / 166225
Scott - 56219 / 56219
Washington - 106045 / 106045

This is a MetroGIS Regionally Endorsed dataset.

Each of the seven Metro Area counties has entered into a multiparty agreement with the Metropolitan Council to assemble and distribute the parcel data for each county as a regional (seven county) parcel dataset.

A standard set of attribute fields is included for each county. The attributes are identical for the point and polygon datasets. Not all attributes fields are populated by each county. Detailed information about the attributes can be found in the MetroGIS Regional Parcels Attributes 2015 document.

Additional information may be available in the individual metadata for each county at the links listed below. Also, any questions or comments about suspected errors or omissions in this dataset can be addressed to the contact person listed in the individual county metadata.

Anoka = http://www.anokacounty.us/315/GIS

Caver = http://www.co.carver.mn.us/GIS

Dakota = http://www.co.dakota.mn.us/homeproperty/propertymaps/pages/default.aspx

Hennepin: http://www.hennepin.us/gisopendata

Ramsey = https://www.ramseycounty.us/your-government/open-government/research-data

Scott = http://www.scottcountymn.gov/1183/GIS-Data-and-Maps

Washington = http://www.co.washington.mn.us/index.aspx?NID=1606