Data set that contains information on archaeological remains of the pre historic settlement of the Letolo valley on Savaii on Samoa. It is built in ArcMap from ESRI and is based on previously unpublished surveys made by the Peace Corps Volonteer Gregory Jackmond in 1976-78, and in a lesser degree on excavations made by Helene Martinsson Wallin and Paul Wallin. The settlement was in use from at least 1000 AD to about 1700- 1800. Since abandonment it has been covered by thick jungle. However by the time of the survey by Jackmond (1976-78) it was grazed by cattle and the remains was visible. The survey is at file at Auckland War Memorial Museum and has hitherto been unpublished. A copy of the survey has been accessed by Olof Håkansson through Martinsson Wallin and Wallin and as part of a Masters Thesis in Archeology at Uppsala University it has been digitised.

Olof Håkansson has built the data base structure in the software from ESRI, and digitised the data in 2015 to 2017. One of the aims of the Masters Thesis was to discuss hierarchies. To do this, subsets of the data have been displayed in various ways on maps. Another aim was to discuss archaeological methodology when working with spatial data, but the data in itself can be used without regard to the questions asked in the Masters Thesis. All data that was unclear has been removed in an effort to avoid errors being introduced. Even so, if there is mistakes in the data set it is to be blamed on the researcher, Olof Håkansson. A more comprehensive account of the aim, questions, purpose, method, as well the results of the research, is to be found in the Masters Thesis itself. Direkt link http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1149265&dswid=9472

Purpose:

The purpose is to examine hierarchies in prehistoric Samoa. The purpose is further to make the produced data sets available for study.

Prehistoric remains of the settlement of Letolo on the Island of Savaii in Samoa in Polynesia

This dataset was developed as part of an Urban Tree Canopy Assessment. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area based on input data years.

Raw data used in MSc Thesis. Available for reproducing methodology

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

High resolution land cover dataset for City of Boston, MA. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The primary sources used to derive this land cover layer were 2013 LiDAR data, 2014 Orthoimagery, and 2016 NAIP imagery. Ancillary data sources included GIS data provided by City of Boston, MA or created by the UVM Spatial Analysis Laboratory. Object-based image analysis techniques (OBIA) were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2500 and all observable errors were corrected.

Credits: University of Vermont Spatial Analysis Laboratory in collaboration with the City of Boston, Trust for Public Lands, and City of Cambridge.

This tidal flat dataset is taken from the MA Department of Environmental Protection's update of its statewide wetlands mapping program which has been mapping wetlands using aerial photography since 1990 (http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/datalayers/depwetlands112000.html). The source aerial digital imagery for this update was flown in spring 2005 during non-tide specified times at a resolution of 0.5 m. Additionally, collateral digital low tide imagery at resolution of 0.4 m, flown during the spring/summer of the period 2006-2010 for the purpose of eelgrass mapping was utilized as necessary. The interpretation of this imagery was analyzed by MA DEP staff in stereo utilizing the Esri ArcGIS software equipped with the PurView stereo viewing tool. Due to the scale of the imagery and the availability of low tide collateral imagery, minimal field-checking was conducted on this 2005 update.

The first geographically widespread metazoans are found in the Avalon assemblage (Ediacaran; 574 – 560 Ma). These early animals were regularly disturbed by sedimentation events such as ash flows and turbidites, leading to an apparent “resetting†of communities. However, it is not clear how biological legacies – remains or survivors of disturbance events – influenced community ecology in the Avalon. Here, we use spatial point process analysis on 19 Avalon palaeocommunities to test whether two forms of biological legacy (fragmentary remains of Fractofusus and surviving frondomorphs) impacted the recolonisation dynamics of Avalon palaeocommunities. We found that densities of Fractofusus were increased around the Fractofusus fragments, suggesting that they helped to recolonise the post-disturbance substrate, potentially contributing to the Fractofusus dominance found in 8 of the 19 palaeocommunities. However, we found no such effects for survivor fronds. Our results suggest that the evoluti..., , # Data from: Recolonisation strategies of early animals in the Avalon (Ediacaran 574 – 560 Ma)

Dataset DOI: 10.5061/dryad.66t1g1kcx

Description of the data and file structure

br5_fractofusus_fragments_coords.xlsx

Coordinate data for complete Fractofusus andersoni and fragments on the BR5 surface in the mistaken point to recreate results from the associated manuscript in Paleobiology

Access information

Other publicly accessible locations of the data:

• Code to reproduce this research is available at: https://github.com/nis38/NPS_dex and https://github.com/nis38/Secondary_Succession

Data was derived from the following source:

• Photomap of Brasier Surface at MPER.,

Description of the calculations used to determine the overall Consequence Spatial Analysis (CSA) risk score for each scoring element in the MSC CSA and the CSA carbon extension.

The zoning boundaries map layer is an integral part of the planning data in the City of Worcester Geographic Information System. This data is used by many City Departments in case review, code enforcement, and long range planning. Planning data layers are accessed by personnel in most City departments for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on planning analysis, spatial analysis, presentation output, and review of proposed development. The zoning boundaries data layer is governed by ordinance and is only changed accordingly. The zoning data layer was digitized by L. Robert Kimball & Associates, Inc. as part of a data conversion project in 1995. Further updates have been made by the City of Worcester since that time to reflect ordinance changes.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost.

Georeferenced raster image dataset representing City of Somerville, Massachusetts, impervious surface areas.

ESRI polygon feature class representing City of Somerville, Massachusetts building footprints. Last updated July 5, 2024.

The spatial allocation of classified roads in the Saarland to the respective street masteries is shown. In addition to the area of the districts, the locations of the branch offices and the associated bases are also displayed. Legal basis: Regulation of the state operation for road construction.

Urban heat islands are small areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services.Dataset SummaryPhenomenon Mapped: Sensing package time step valuesUnits: decimal degrees Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried.Cities IncludedBaltimore, Boise, Boston, Fort Lauderdale, Honolulu, Los Angeles, Nampa, Oakland-Berkeley, Portland, Richmond, Sacramento, San Bernardino, San Juan, Victorville, Washington, West Palm Beach, Worcester, Charleston and YonkersCities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Boston MAAfternoon air temperatures in cities

Data content: Based on the VIIRS (Visible Infrared Imaging Radiometer Suite) sensor medium resolution 375mNPP-VIIRS active thermal anomaly data, field research, and other big data of the earth, we constructed the global continental region of high-energy-consuming industrial heat source product data set, totaling 25,544 data. After validation 23232 items are industrial heat source objects, and the recognition accuracy is 90.95%. The output format is shapefile.

Time range of data:2012-2021
Spatial scope: Global continental area
Projection method: WGS84
Volume of data: The total volume of data is about 3346kb.
Type of data: Vector

Species' threat assessments produce generalized threat impact scores, often by considering regional-scale representations of threats. Cities, on the other hand, produce municipal-scale, high resolution data that are proxies for threats; furthermore, cities in mega-diverse regions are home to a high number of threatened species. Prioritization of conservation action is biased for where more information is known (about the ecosystem), and where a positive outcome can be anticipated. Eight Cape peninsula amphibian species have a threatened conservation status. They are isolated on highlands or are restricted to remnant and suburban habitats, dependent on both urban and protected terrestrial and freshwater habitats found in the City of Cape Town and Table Mountain National Park.

In Chapter Two, I used spatial data (shapefiles) to represent threats in a Geographic Information System to spatially define threats to eight amphibian species (five lowland, three upland). I used two approaches: weighted and un-weighted by a threat impact-score, to produce five indices of local threats. The Micro Frog (Microbatrachella capensis) is assessed as the most threatened peninsula frog species by three of the five indices considered. The results show that for lowland species, the threat-class of greatest extent is 'Residential and commercial development'. The three lowland species most exposed to this threat are M. capensis (100% exposed to potential development), Breviceps gibbosus (55.6% of its 8.5 km2 putative peninsula distribution), and Sclerophrys pantherina (38.4% of its 199.7 km2 distribution). The Compounded and the General Threat Index correlate to the (global) Redlist Index (P < 0.05); but no correlation to the regional Red Listing, indicating congruency of threats and threat status.

The Critically Endangered Table Mountain Ghost Frog (Heleophryne rosei) is torrent adapted, and found only on the Table Mountain massif. CapeNature monitors tadpoles, and SANParks monitors (selected) stream parameters. In Chapter Three, I analyse water-habitat monitoring data (controlled for altitude) to show where threats of habitat alteration, drought, or temperature extremes may affect the H. rosei metapopulation. Permanence of water-flow and water temperature are shown to be very highly significant predictors of tadpole presence (p = 0.0005, r = 0.78). The lower the water temperature, the more likely tadpoles are present. Streams with a mean summer temperature greater than 17.2°C (n=3) at 400 to 300 meters above sea level were found to have no tadpoles at this altitude. Permanence of water flow is significant, as tadpoles need more than one year to reach metamorphosis. Summer water temperatures over an average of 17.2°C should be a red-flag for management authorities responsible for bulk-water supply, threat mitigation efforts, and biodiversity conservation.

Spatial indices of threat are useful to illustrate the relative exposure to threats at a local (city) scale. Threats to different lowland amphibians are similar (e.g. residential and commercial development), which varies from the mutual threats to different upland amphibians. Fundamental to stream species' conservation is water supply and demand management, while upland terrestrial species are most affected by veld age and invasive alien flora. Some threats are common for both areas (e.g. invasive alien species).

ESRI point feature class representing City of Somerville, Massachusetts police station locations.

Heat islands are areas where temperatures are unnaturally high - usually due to dense buildings, expansive hard surfaces, or a lack of tree cover or greenspace. People living in these communities are exposed to more dangerous conditions, especially as daytime high and nighttime low temperatures increase over time. NOAA Climate Program Office and CAPA Strategies have partnered with cities around the United States to map urban heat islands. Using Sentinel-2 satellite thermal data along with on-the-ground sensors, air temperature and heat indexes are calculated for morning, afternoon, and evening time periods. The NOAA Visualization Lab, part of the NOAA Satellite and Information Service, has made the original heat mapping data available as dynamic image services. Dataset SummaryPhenomenon Mapped: air temperatureUnits: degrees Fahrenheit Cell Size: 30 metersPixel Type: 32 bit floating pointData Coordinate Systems: WGS84 Mosaic Projection: WGS84 Extent: cities within the United StatesSource: NOAA and CAPA StrategiesPublication Date: September 20, 2021 What can you do with this layer?This imagery layer supports communities' UHI spatial analysis and mapping capabilities. The symbology can be manually changed, or a processing template applied to the layer will provide a custom rendering. Each city can be queried. Related layers include Morning Air Temperature and Evening Air Temperature. Cities IncludedBoulder, CO Brooklyn, NY Greenwich Village, NY Columbia, SC Columbia, MO Columbus, OH Knoxville, TN Jacksonville, FL Las Vegas, NV Milwaukee, WI Nashville, TN Omaha, NE Philadelphia, PA Rockville, MD Gaithersburg, MD Takoma Park, MD San Francisco, CA Spokane, WA Abingdon, VA Albuquerque, NM Arlington, MA Woburn, MA Arlington, VA Atlanta, GA Charleston, SC Charlottesville, VA Clarksville, IN Farmville, VA Gresham, OR Harrisonburg, VA Kansas City, MO Lynchburg, VA Manhattan, NY Bronx, NY Newark, NJ Jersey City, NJ Elizabeth, NJ Petersburg, VA Raleigh, NC Durham, NC Richmond, VA Richmond, IN Salem, VA San Diego, CA Virginia Beach, VA Winchester, VA Austin, TX Burlington, VT Cincinnati, OH Detroit, MI El Paso, TX Houston, TX Jackson, MS Las Cruces, NM Miami, FL New Orleans, LA Providence, RI Roanoke, VA San Jose, CA Seattle, WA Vancouver, BC Canada Boston, MA Fort Lauderdale, FL Honolulu, HI Boise, ID Nampa, ID Los Angeles, CA Yonkers, NY Oakland, CA Berkeley, CA San Juan, PR Sacramento, CA San Bernardino, CA Victorville, CA West Palm Beach, FL Worcester, MA Washington, D.C. Baltimore, MD Portland, OR Cities may apply to be a part of the Heat Watch program through the CAPA Strategies website. Attribute Table Informationcity_name: Afternoon Air Temperature Observations in Floating-Point (°F) Acknowledgements: This research was made possible with support from the Institute of Sustainable Solutions at Portland State University, and the James F. & Marion L. Miller Foundation. The authors also acknowledge the teams of volunteers from each of the study areas who carefully and dutifully completed their traverses and contributed to the positive outcomes and success of these projects. The authors further acknowledge GroundworkRVA in Richmond, the National Aquarium in Baltimore, Science Museum of Virginia, and the Department of Energy and Environment in Washington, D.C. for opening up their facilities for the volunteer training orientation sessions.

ESRI polygon feature class representing City of Somerville, Massachusetts fire response districts.

ESRI polygon feature class representing the City of Somerville, Massachusetts city boundary.

ESRI point feature class representing City of Somerville, Massachusetts fire station locations.

Impervious layers are a compilation of GIS layers which include buildings, structures, paved surfaces (road, sidewalk, parking lots, driveways), patio, concrete pads, plaza, transmission tower pad, electric boxes, and irrigation devices, This is a snapshot from April 14, 2010City of Cambridge, MA GIS basemap development project encompasses the land area of City of Cambridge with a 200 foot fringe surrounding the area and Charles River shoreline towards Boston. The basemap data was developed at 1" = 40' mapping scale using digital photogrammetric techniques. Planimetric features; both man-made and natural features like vegetation, rivers have been depicted. These features are important to all GIS/mapping applications and publication. A set of data layers such as Buildings, Roads, Rivers, Utility structures, 1 ft. interval contours are developed and represented in the geodatabase. The features are labeled and coded in order to represent specific feature class for thematic representation and topology between the features is maintained for an accurate representation at the 1:40 mapping scale for both publication and analysis. The basemap data has been developed using procedures designed to produce data to the National Standard for Spatial Data Accuracy (NSSDA) and is intended for use at 1" = 40 ' mapping scale.Explore all our data on the Cambridge GIS Data Dictionary.Attributes NameType DetailsDescription TYPE type: Stringwidth: 50precision: 0 Feature class which was used to create the impervious surfaces layer