MIT light grey basemap used for thematic mapping.

Die Hochwasserrisikogebiete der Hochwasserszenarien HQ10, HQ20 (nur Elbe-Hauptschlauch), HQ100 und HQextrem (Berechnung des HQ200 ohne Wirksamkeit von Hochwasserschutzeinrichtungen) wurden im Land Brandenburg unterschiedlich ermittelt. Zum Einsatz kamen in der Regel hydronumerische Modelle (gekoppelte 1D/2D Modelle, 2D Modelle) mit stationärem Ansatz. Ausnahmen bilden die Hochwasserrisikogebiete an den Bundeswasserstraßen Elbe/Prignitz und Havel. Diese wurden mittels einer GIS-technischen Ausspiegelung auf der Grundlage von Pegelzeitreihen, Wasserstandslängsschnitten, der Stationierungen der Bundeswasserstraßen und dem DGM mit Deichen ermittelt.Herkunft:Gewässernetz Brandenburg (Datenbestand LfU) Vermessung von Gewässern, Hochwasserschutzanlagen und anderen wasserwirtschaftlichen Anlagen (Datenbestand LfU) Hydrologische Daten der Fließgewässer (Datenbestand LfU)Datenquellen:- DGM1 - Orthofotos (DOP40) - Gewässernetz Brandenburg (Datenbestand LfU) - Vermessung von Gewässern, Hochwasserschutzanlagen und anderen wasserwirtschaftlichen Anlagen (Datenbestand LfU) - Hydrologische Daten der Fließgewässer (Datenbestand LfU)Herstellungsprozess:Nach Ermittlung der Hochwasserrisikogebiete wurden die Anschlaglinien und -polygone aller Hochwasserrisikogebiete mit dem PEAK-Algorithmus (in ESRI-ArcGIS 9.3.1) und einer Toleranz von 5 m geglättet. Aus Gründen der Datenverarbeitung mussten im Anschluss mit Hilfe des Douglas-Algorithmus (in ESRI-ArcGIS 10.1) die Stützpunkte reduziert werden. Die maximale Toleranz bei der Nutzung des Douglas-Algorithmus beträgt 0,1 m. Die Geometrie und Topologie des Datensatzes bleiben innerhalb dieser Toleranz erhalten. Es werden alle Stützpunkte, deren Entfernung eine Abweichung unterhalb der Generalisierungs-Toleranz verursacht, gelöscht. Die Position der verbleibenden Stützpunkte wird nicht verändert. Für die leichtere Handhabung wurden die gemeldeten Daten nach Hochwasserszenarien getrennt und mit Textattributen versehen. Weiterhin wurden die größten Polygone an Engstellen geteilt, um die Nutzung im Desktop GIS zu verbessern.Die Daten Stammen vom Geobasis Brandenburg und wurden heruntergeladen, umprojiziert und anschließend veröffentlicht.

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

MIT Restroom Centroids

Polygonale Klasse, die die Isowertbereiche im Zusammenhang mit Feinstaub (PM10) auf regionaler Skala anzeigt. Daten über die gesamte Molise-Region.

A snapshot of the Restroom Polygons exported from MIT space accounting

Ladesäulen oder Ladestationen dienen dem Laden von Elektrofahrzeugen. Sie sind konventionellen Zapfsäulen nachempfunden und bieten in der Regel verschiedene Kabelverbindungen. Dieser Dienst enthält die Ladesäulen, die der Ladesäulenverordnung (LSV) genügen. Die Liste beinhaltet also die Ladeeinrichtungen aller Betreiberinnen und Betreiber, die das Anzeigeverfahren der Bundesnetzagentur vollständig abgeschlossen und einer Veröffentlichung im Internet zugestimmt haben. Die Zahl der öffentlich zugänglichen Ladeeinrichtungen in Deutschland ist daher größer als hier dargestellt.Die Daten stammen von der Seite der Bundesnetzagentur (Stand 01.12.2024) und wurden mit ArcGIS Pro auf WGS84 Web Mercator umprojiziert. Die Punkte wurden mit den Koordinaten aus der BnA-Tabelle erstellt. In manchen Fällen stimmen die Koordinaten nicht mit der Adresse überein.Neben den Lagekoordinaten sind Informationen zum Betreiber, der Inbetriebnahme, der Anschlussleistung in Kilowatt, der Art der Ladeeinrichtung, der Anzahl der Ladepunkte sowie der Steckertypen mit einer Angabe zur Kilowattleistung enthalten.Bitte beachten Sie die unten aufgeführten Nutzungsbestimmungen der Bundesnetzagentur.

This file contains the arrival and departure events for buses for calendar year 2018. Due to data collection issues, data is not guaranteed to be complete for any stop or date.Data Dictionary:

Name
Description
Data Type
Example


service_date
Date on which the trip took place. Service dates run from around 2:00AM - 1:59:59AM 
Date
2019-12-31


route_id
Route Id
String
01


direction_id
Identifies whether the trip is traveling inbound or outbound
String
Inbound


half_trip_id
Identification for the one way trip
Integer
40836717


stop_id
GTFS-compatible stop
Integer
75


time_point_id
The code for the timepoint
String
mit


time_point_order
The order of this timepoint in the trip
Integer
4


point_type
Identifies whether the stop is the starting point, midpoint, or endpoint for the trip 
String
Midpoint


standard_type
Identifies whether the trip should be evaluated on the scheudle standard or headway standard. 
String
Headway


scheduled
The time the trip was scheduled to depart the stop. The scheduled time should not be used to evaluated reliability if this trip is evaluated on the headway standard. Only the headway and run time should be used per the Service Delivery Policy.
Time
12:30 AM


actual
The time the trip actually departed the timepoint.
Time
12:29 AM

Integer
-60


scheduled_headway
The scheduled time between the trip and the previous trip at the stop, in seconds. NULL if this trip is evaluated on the schedule standard.

Integer
1200


headway
The actual time between the trip and the previous trip at the stop, in seconds. NULL if this trip is evaluated on the schedule standard or if this is the last stop on the trip (endpoint). Endpoints are evaluated by whether the trip runtime is within 120% of the scheduled run time.

Integer
1163

MassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

Maps out the SBB lines as a geometric line.

This feature layer is part of SDGs Today. Please see sdgstoday.orgPromoting well-being is one of the key targets of Sustainable Development Goals at the United Nations. Many governments worldwide are incorporating subjective well-being (SWB) indicators to complement traditional objective and economic metrics. Our Twitter Sentiment Geographical Index (TSGI) can provide a high granularity monitor of well-being worldwide.This dataset is a joint effort of the Sustainable Urbanization Lab at MIT and Center for Geographic Analysis at Harvard.

This map layer includes sand and gravel operations in the United States. These data were obtained from information reported voluntarily to the USGS by the aggregate producing companies. The data represent commodities covered by the Minerals Information Team (MIT) of the U.S. Geological Survey, and the operations are those considered active in 2002 with production greater than 50,000 tons, which are non-government, non-portable, and surveyed by the MIT. This is a replacement for the January 2001 map layer.

This file contains the arrival and departure events for buses between January and December 2020. Due to data collection issues, data is not guaranteed to be complete for any stop or date.Data Dictionary:

Name
Description
Data Type
Example


service_date
Date on which the trip took place. Service dates run from around 2:00AM - 1:59:59AM 
Date
2019-12-31


route_id
Route Id
String
01


direction_id
Identifies whether the trip is traveling inbound or outbound
String
Inbound


half_trip_id
Identification for the one way trip
Integer
40836717


stop_id
GTFS-compatible stop
Integer
75


time_point_id
The code for the timepoint
String
mit


time_point_order
The order of this timepoint in the trip
Integer
4


point_type
Identifies whether the stop is the starting point, midpoint, or endpoint for the trip 
String
Midpoint


standard_type
Identifies whether the trip should be evaluated on the scheudle standard or headway standard. 
String
Headway


scheduled
The time the trip was scheduled to depart the stop. The scheduled time should not be used to evaluated reliability if this trip is evaluated on the headway standard. Only the headway and run time should be used per the Service Delivery Policy.
Time
12:30 AM


actual
The time the trip actually departed the timepoint.
Time
12:29 AM


scheduled_headway
The scheduled time between the trip and the previous trip at the stop, in seconds. NULL if this trip is evaluated on the schedule standard.

Integer
1200


headway
The actual time between the trip and the previous trip at the stop, in seconds. NULL if this trip is evaluated on the schedule standard or if this is the last stop on the trip (endpoint). Endpoints are evaluated by whether the trip runtime is within 120% of the scheduled run time.

Integer
1163

MassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

Der Service stellt die administrativen Kreisgrenzen zum Stand 01. Januar 2023 ohne Wasserflächen im Maßstab 1:250.000 zur Verfügung.Der Datenbestand entspricht den Georeferenzdaten VG250-Ebenen des BKG Bundesamtes für Kartographie und Geodäsie auf Ebene Kreis (KRS) mit folgender Geometrieänderung:Es wurden nur die Land-Geometrien (GF = 4) übernommen und auf Web Mercator umprojiziert.VG250 Datensatzbeschreibung des BKGEnglish This service provides the administrative County boundaries of Germany as of January 1st, 2023.This layer only contains land surfaces at a 1:250,000 scale. It corresponds to the georeferenced VG250 data of the BKG Federal Agency for Cartography and Geodesy.Only the land geometries (GF=4) were adopted and projected to Web Mercator.

This file contains the arrival and departure events for buses for calendar 2019. Due to data collection issues, data is not guaranteed to be complete for any stop or date.Data Dictionary:

Name
Description
Data Type
Example


service_date
Date on which the trip took place. Service dates run from around 2:00AM - 1:59:59AM 
Date
2019-12-31


route_id
Route Id
String
01


direction_id
Identifies whether the trip is traveling inbound or outbound
String
Inbound


half_trip_id
Identification for the one way trip
Integer
40836717


stop_id
GTFS-compatible stop
Integer
75


time_point_id
The code for the timepoint
String
mit


time_point_order
The order of this timepoint in the trip
Integer
4


point_type
Identifies whether the stop is the starting point, midpoint, or endpoint for the trip 
String
Midpoint


standard_type
Identifies whether the trip should be evaluated on the scheudle standard or headway standard. 
String
Headway


scheduled
The time the trip was scheduled to depart the stop. The scheduled time should not be used to evaluated reliability if this trip is evaluated on the headway standard. Only the headway and run time should be used per the Service Delivery Policy.
Time
12:30 AM


actual
The time the trip actually departed the timepoint.
Time
12:29 AM


scheduled_headway
The scheduled time between the trip and the previous trip at the stop, in seconds. NULL if this trip is evaluated on the schedule standard.

Integer
1200


headway
The actual time between the trip and the previous trip at the stop, in seconds. NULL if this trip is evaluated on the schedule standard or if this is the last stop on the trip (endpoint). Endpoints are evaluated by whether the trip runtime is within 120% of the scheduled run time.

Integer
1163

MassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

This file contains the arrival and departure events for buses up to the most recent completed month of 2022. Due to data collection issues, data is not guaranteed to be complete for any stop or date.Data Dictionary:

Name
Description
Data Type
Example


service_date
Date on which the trip took place. Service dates run from around 2:00AM - 1:59:59AM 
Date
2019-12-31


route_id
Route Id
String
01


direction_id
Identifies whether the trip is traveling inbound or outbound
String
Inbound


half_trip_id
Identification for the one way trip
Integer
40836717


stop_id
GTFS-compatible stop
Integer
75


time_point_id
The code for the timepoint
String
mit


time_point_order
The order of this timepoint in the trip
Integer
4


point_type
Identifies whether the stop is the starting point, midpoint, or endpoint for the trip 
String
Midpoint


standard_type
Identifies whether the trip should be evaluated on the scheudle standard or headway standard. 
String
Headway


scheduled
The time the trip was scheduled to depart the stop. The scheduled time should not be used to evaluated reliability if this trip is evaluated on the headway standard. Only the headway and run time should be used per the Service Delivery Policy.
Time
12:30 AM


actual
The time the trip actually departed the timepoint.
Time
12:29 AM


scheduled_headway
The scheduled time between the trip and the previous trip at the stop, in seconds. NULL if this trip is evaluated on the schedule standard.

Integer
1200


headway
The actual time between the trip and the previous trip at the stop, in seconds. NULL if this trip is evaluated on the schedule standard or if this is the last stop on the trip (endpoint). Endpoints are evaluated by whether the trip runtime is within 120% of the scheduled run time.

Integer
1163

MassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.

swissBUILDINGS3D 3.0 Beta is a vector based dataset provided by swisstopo which describes buildings as 3D models with roof geometries and roof overhangs. The detailed roof structures are recorded in three dimensions and enhanced with additional information as attributes. The high degree of detail in all three dimensions, together with the high coverage and realistic rendering of the building volumes, make this product a valuable basic dataset for a large range of applications. swissBUILDINGS3D 3.0 is updated every six years.With the current relesase of swissBUILDINGS3D 3.0 Beta, swisstopo provides building models structred according to the federal building identifier (EGID) and containing the EGID as additional information. The data are available in the cantons AG, AI, AR, BE, BL, BS, GL, JU, SG, SZ, TG and the city of Zurich.Application examplesThis scene layer can be applied in a broad range of areas, and constitutes an ideal planning and visualization tool for planners, environmental engineers, public authorities, architects, etc. For example, this data offers the ideal background data for the following use cases:3D visualizations (e.g. tourism, marketing, information)Basis of urban and spatial planning, residential development projects, mobility, telecommunications or energyVisibility and shadow analysesCalculation of solar potentialSimulation of natural disastersAnalyses of distribution (noise, air pollutants, electromagnetic radiation)Ecology and urban climatologyAttributes with identifiers from the Swiss official commune register were added and allow filtering by municipalities, districts or cantons.This scene layer is provided in Web Mercator projection (EPSG 3857). The source data can be downloaded from swisstopo's website.Data vintage: December 2024. The service is updated semiannually.

Lisa Stähli, Senior Product Engineer ArcGIS Urban, Esri R&D Center Zurich

Dieser Feature Layer enthält Hexagone mit einer Nord-Süd-Ausdehnung von 6,25 km. Er kann als Eingabe-Layer für Analysen dienen wie Punkte aggregieren. Dieses Werkzeug verwendet die Hexagone, um z.B. eine Reihe von Punkt-Features zusammenzufassen. Dabei können statistische Informationen wie die Anzahl der Punkte aber auch der Durchschnittswert eines bestimmten Punkt-Attributs an die Hexagon-Fläche gebracht werden. Auf diese Weise können Punktinformationen, die zu zahlreich für eine Darstellung in der Karte sind, aggregiert und in ansprechender Weise visualisiert werden.Die Darstellung in Form von Hexagonen wird vom Betrachter oft als weniger „hart“ empfunden im Vergleich zu Quadraten.Sie können mit ArcGIS auch eigene Hexagone erstellen. Wie, das beschreibt die Online-Hilfe unter Mosaik generieren.

Der Service stellt die administrativen Bundesländergrenzen mit Einwohnerzahl zum Stand 31. Dezember 2020 ohne Wasserflächen im Maßstab 1:250.000 zur Verfügung.Der Datenbestand entspricht den Georeferenzdaten VG250-Ebenen des BKG Bundesamtes für Kartographie und Geodäsie auf Ebene Bundesland (LAN) mit folgender Geometrieänderung:Es wurden nur die Land-Geometrien (GF = 3 oder 4) übernommen, aufgelöscht und auf Web Mercator umprojiziert.VG250 Datensatzbeschreibung des BKGEnglish This service provides the administrative Federal State boundaries of Germany with population number as of December 31st, 2020.This layer only contains land surfaces at a 1:250,000 scale. It corresponds to the georeferenced VG250 data of the BKG Federal Agency for Cartography and Geodesy.Only the land geometries (GF=3 or GF=4) were adopted, dissolved and projected to Web Mercator.

This data set includes miscellaneous industrial minerals operations in the United States. The data represent commodities covered by the Minerals Information Team (MIT) of the U.S. Geological Survey. The mineral operations are plants and (or) mines surveyed by the MIT and considered currently active in 2003. This is a replacement for the July 2004 map layer.The data is legacy and not expected to be updated. It is being provided as the best available until Mineral Resources identifies an alternative data source.