Data comes from American Community Survey 5-year estimate 2019-2023 table C16001. It was joined to the Census Tract boundaries by Boulder County GIS. This data is used in a Language Access Map Viewer.

The dictionary includes about 1650 terms and concepts (in Armenian, Russian and English) used in forest and landscaping sectors with a brief explanation in Armenian.Citation:J.H. Vardanyan, H.T. Sayadyan, Armenian-Russian-English Dictionary of Forest Terminology, Publishing House of the Institute of Botany of NAS RA, Yerevan, 2008.

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood

Urban fabrics for the Helsinki region 2016, 2030 and 2050 GIS dataset represents modelled urban fabric areas (walking, transit, and automobile urban fabrics) in the Helsinki city region (14 municipalities) in Finland. The data is associated with the regional MAL 2019 (land use, housing, and transport) work and was developed at the Finnish Environment Institute (Syke). The method used to produce the data has also been applied to other city regions in Finland (Helminen et al., 2020) and is an application of Newman et al.'s (2016) theory of three urban fabrics. The method is based on the overlay analysis of three variables: population and job density, accessibility to local services, and public transportation supply, with threshold values set for each variable. The definition of threshold values is based on previous applications of urban fabrics (Ristimäki et al., 2017) and a workshop conducted for urban planning and transportation professionals in the Helsinki metropolitan area. All accessibility measures used in creating the data were calculated as Euclidean distances.

The data was created using ArcMap Advanced software (version 10.6) and includes shapefiles for each modeling year's urban structures (UF_2016, UF_2030, and UF_2050) as well as description styles (UF_Fi.qml and UF_En.qml) in Finnish and English for the QGIS software. The names of the structures are in the fields 'Kudos' (in Finnish) and 'UrbFab' (in English). The coordinate system of the data is EPSG:3067. Detailed descriptions of the data and the method can be found in the report 'Helsingin seudun kaupunkikudokset 2016, 2030 a 2050' (Tiitu et al., 2018, in Finnish) and in the downloadable ReadMe files below (both in Finnish and English).

Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050 -paikkatietoaineisto

Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050 -paikkatietoaineisto kuvaa mallinnettuja kaupunkikudosten alueita (jalankulku-, joukkoliikenne- ja autokaupunki) Helsingin seudun (14 kuntaa) alueelta Suomesta. Aineisto liittyy seudun MAL 2019 -työhön, ja se on kehitetty Suomen ympäristökeskuksessa (Syke). Menetelmää, jolla aineisto on tuotettu, on sovellettu myös muille Suomen kaupunkiseuduille (Helminen ym. 2020), ja se on sovellutus Newmanin ym. (2016) kolmen kaupunkikudoksen teoriasta. Menetelmä perustuu päällekkäisanalyysiin kolmesta muuttujasta: asukas- ja työpaikkatiheys, lähikaupan saavutettavuus ja joukkoliikenteen tarjonta, sekä muuttujille asetettuihin kynnysarvoihin. Kynnysarvojen määrittely perustui kaupunkikudosten aiempiin sovellutuksiin (Ristimäki ym. 2017) sekä Helsingin seudun maankäytön ja liikenteen suunnittelijoille suunnattuun työpajaan. Kaikki aineiston muodostamiseen käytetyt saavutettavuudet on laskettu linnuntie-etäisyyksinä.

Aineisto on muodostettu ArcMap Advanced -ohjelmistolla (versio 10.6.) ja se sisältää shp-tiedostot kunkin mallinnusvuoden kaupunkikudoksille (UF_2016, UF_2030 ja UF_2050) sekä kuvaustekniikan (UF_Fi.qml ja UF_En.qml) suomeksi ja englanniksi QGIS-ohjelmistolle. Kudosten nimet ovat sarakkeissa Kudos (suomeksi) ja UrbFab (englanniksi). Aineiston koordinaattijärjestelmä on EPSG:3067. Aineiston ja menetelmän tarkka kuvaus on luettavissa raportista Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050 (Tiitu ym. 2018) sekä alla ladattavista ReadMe-tiedostoista.

References

Helminen V., Tiitu M., Kosonen, L. & Ristimäki, M. (2020). Identifying the areas of walking, transit and automobile urban fabrics in Finnish intermediate cities. Transportation Research Interdisciplinary Perspectives 8, 100257. https://doi.org/10.1016/j.trip.2020.100257

Newman, L. Kosonen & J. Kenworthy (2016). Theory of urban fabrics; planning the walking, transit/public transport and automobile/motor car cities for reduced car dependency. Town planning Review 87 (4): 429–458. http://hdl.handle.net/20.500.11937/11247

Ristimäki M., Tiitu M., Helminen V., Nieminen H., Rosengren K., Vihanninjoki V., Rehunen A., Strandell A., Kotilainen A., Kosonen L., Kalenoja H., Nieminen J., Niskanen S. & Söderström P. (2017). Yhdyskuntarakenteen tulevaisuus kaupunkiseuduilla – Kaupunkikudokset ja vyöhykkeet. Suomen ympäristökeskuksen raportteja 4/2017. Suomen ympäristökeskus, Helsinki. http://hdl.handle.net/10138/176782

Tiitu M., Helminen V., Nurmio K. & Ristimäki M. (2018). Helsingin seudun kaupunkikudokset 2016, 2030 ja 2050. MAL 2019 publication. https://www.hsl.fi/sites/default/files/uploads/helsingin_seudun_kaupunkikudokset_loppuraportti_27082018_0.pdf

License / Lisenssi

Syke applies Creative Commons By 4.0 International license for open datasets.
This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. The source references for credits can be found in the metadata of each data product.

Suomen ympäristökeskuksen (Syke) avointen aineistojen käyttölupa on Creative Commons Nimeä 4.0 Kansainvälinen.
Lisenssin kohteena olevaa dataa voi vapaasti käyttää kaikin mahdollisin tavoin edellyttäen, että datan lähde mainitaan: Lisenssinantajan nimi ja aineiston nimi.

Credits / Lähdemerkintä

Urban Fabrics for the Helsinki Region / Source: Finnish Environment Institute Syke 2018.
Where applicable, please also cite the references listed above.

Helsingin seudun kaupunkikudokset / Lähde: Syke 2018.
Viittaa myös soveltuvin osin yllä listattuihin lähteisiin, jos hyödynnät näitä aineistoja esimerkiksi raporteissa tai tutkimusartikkeleissa.

The Coastal Overview data layers identifies the lead authority for the management of discrete stretches of the English coast as defined by the Seaward of the Schedule 4 boundary of the Coastal Protection Act 1949. The data are intended as a reference for GIS users and Coastal Engineers with GIS capability to identify the responsible authority or whether the coast is privately owned. The information has been assigned from the following sources, listed in by preference: Shoreline Management Plans 1; Environment Agency’s RACE database; Consultation with Coastal Business User Group and Local Authority Maritime records where possible. A confidence rating is attributed based on where the data has been attributed from and the entry derived from the source data. The following data is intended as a reference document for GIS users and Coastal Engineers with GIS capability to identify the responsible authority and the assigned EA Coastal Engineer so as to effectively manage the coast for erosion and flooding. The product comprises 3 GIS layers that are based on the OS MasterMap Mean High Watermark and consists of the following data layers that are intended to be displayed as with the confidence factor that the information is correct. Coastal Overview Map [Polyline] –details the Lead Authority, EA Contact and other overview information for coast sections; Coastal Overview Map [Point] – shows the start point of the discrete stretch of coast and the lead authority; and Coastal Legislative Layer [Polyline] - represents the predominant risk; flooding or erosion, which are assigned to each section of the coastline.

Office for National Statistics' national and subnational Census 2021. Proficiency in EnglishThis dataset provides Census 2021 estimates that classify usual residents in England and Wales by their proficiency in English. The estimates are as at Census Day, 21 March 2021. Proficiency in English language definition: How well people whose main language is not English (English or Welsh in Wales) speak English.Comparability with 2011: Highly comparable This data is issued at (BGC) Generalised (20m) boundary type for:Country - England and WalesRegion - EnglandUTLA - England and WalesLTLA - England and WalesWard - England and WalesMSOA - England and WalesLSOA - England and WalesOA - England and WalesIf you require the data at full resolution boundaries, or if you are interested in the range of statistical data that Esri UK make available in ArcGIS Online please enquire at content@esriuk.com.The data services available from this page are derived from the National Data Service. The NDS delivers thousands of open national statistical indicators for the UK as data-as-a-service. Data are sourced from major providers such as the Office for National Statistics, Public Health England and Police UK and made available for your area at standard geographies such as counties, districts and wards and census output areas. This premium service can be consumed as online web services or on-premise for use throughout the ArcGIS system.Read more about the NDS.

🇺🇸 미국 English This dataset shows the point locations of dams in Allegheny County. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Energy Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: 2003 Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: none Other: none Related Document(s): Data Dictionary (none) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

Reused dataset from https://data.depositar.io/dataset/place-names-in-west-central-district-of-tainanPlace Names on Ancient Maps of West Central District of Tainan, Taiwan來源研究計畫：空間資訊科學與跨領域研究─台江內海地區的人文社會經濟發展與環境變遷-用以處理台江內海地區時空資訊之協同研究平台的探索與建立 (http://gis.rchss.sinica.edu.tw/taijiang/子計畫四)English Translation for Place Name and Type via Google Translation

This page provides data for the 3rd Grade Reading Level Proficiency performance measure.The dataset includes the student performance results on the English/Language Arts section of the AzMERIT from the Fall 2017 and Spring 2018. Data is representive of students in third grade in public elementary schools in Tempe. This includes schools from both Tempe Elementary and Kyrene districts. Results are by school and provide the total number of students tested, total percentage passing and percentage of students scoring at each of the four levels of proficiency. The performance measure dashboard is available at 3.07 3rd Grade Reading Level Proficiency.Additional InformationSource: Arizona Department of EducationContact: Ann Lynn DiDomenicoContact E-Mail: Ann_DiDomenico@tempe.govData Source Type: Excel/ CSVPreparation Method: Filters on original dataset: within "Schools" Tab School District [select Tempe School District and Kyrene School District]; School Name [deselect Kyrene SD not in Tempe city limits]; Content Area [select English Language Arts]; Test Level [select Grade 3]; Subgroup/Ethnicity [select All Students] Remove irrelevant fields; Add Fiscal YearPublish Frequency: Annually as data becomes availablePublish Method: ManualData Dictionary

🇺🇸 미국 English This dataset portrays the boundaries of the County Council Districts in Allegheny County. The dataset is based on municipal boundaries and City of Pittsburgh ward boundaries and was updated as the result of reapportionment in September 2002. It has also been attributed with the current representatives' names. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Civic Vitality and Governance Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: 2002-present Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: none Other: none Related Document(s): Data Dictionary (none) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

🇺🇸 미국 English This dataset demarcates the municipal boundaries in Allegheny County. Data was created to portray the boundaries of the 130 Municipalities in Allegheny County the attribute table includes additional descriptive information including Councils of Government (COG) affiliation (regional governing and coordinating bodies comprised of several bordering municipalities), School District, Congressional District, FIPS and County Municipal Code and County Council District. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Civic Vitality and Governance Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: current Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: none Other: none Related Document(s): Data Dictionary (none) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

The Coastal Overview data layers identifies the lead authority for the management of discrete stretches of the English coast as defined by the Seaward of the Schedule 4 boundary of the Coastal Protection Act 1949. The data are intended as a reference for GIS users and Coastal Engineers with GIS capability to identify the responsible authority or whether the coast is privately owned. The information has been assigned from the following sources, listed in by preference: Shoreline Management Plans 1; Environment Agency’s RACE database; Consultation with Coastal Business User Group and Local Authority Maritime records where possible. A confidence rating is attributed based on where the data has been attributed from and the entry derived from the source data. The following data is intended as a reference document for GIS users and Coastal Engineers with GIS capability to identify the responsible authority and the assigned EA Coastal Engineer so as to effectively manage the coast for erosion and flooding. The product comprises 3 GIS layers that are based on the OS MasterMap Mean High Watermark and consists of the following data layers that are intended to be displayed as with the confidence factor that the information is correct. Coastal Overview Map [Polyline] –details the Lead Authority, EA Contact and other overview information for coast sections; Coastal Overview Map [Point] – shows the start point of the discrete stretch of coast and the lead authority; and Coastal Legislative Layer [Polyline] - represents the predominant risk; flooding or erosion, which are assigned to each section of the coastline. Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved.Contains Ordnance Survey data © Crown copyright and database rights

State and Local Public Health Departments Governmental public health departments are responsible for creating and maintaining conditions that keep people healthy. A local health department may be locally governed, part of a region or district, be an office or an administrative unit of the state health department, or a hybrid of these. Furthermore, each community has a unique "public health system" comprising individuals and public and private entities that are engaged in activities that affect the public's health. (Excerpted from the Operational Definition of a functional local health department, National Association of County and City Health Officials, November 2005) Please reference http://www.naccho.org/topics/infrastructure/accreditation/upload/OperationalDefinitionBrochure-2.pdf for more information. Facilities involved in direct patient care are intended to be excluded from this dataset; however, some of the entities represented in this dataset serve as both administrative and clinical locations. This dataset only includes the headquarters of Public Health Departments, not their satellite offices. Some health departments encompass multiple counties; therefore, not every county will be represented by an individual record. Also, some areas will appear to have over representation depending on the structure of the health departments in that particular region. Visiting nurses are represented in this dataset if they are contracted through the local government to fulfill the duties and responsibilities of the local health organization. Effort was made by TechniGraphics to verify whether or not each health department tracks statistics on communicable diseases. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard fields populated by TechniGraphics. Double spaces were replaced by single spaces in these same fields. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on this field, the oldest record dates from 11/25/2009 and the newest record dates from 12/28/2009

The RUM project team consisted of researchers and collaborators who contributed at various stages of the project's development, each engaging with its conceptual, technical, and fieldwork components in different capacities. The final structure, form and content of the RUM Archive was shaped by Natalia Zawiejska (PI), Łukasz Stypuła, Radosław Piskorski (GIS), and Aleksandra Jasińska (GIS). The field data collection was carried out by local teams in Kraków, Lublin, and Gdańsk. Every multimedia file (photo, audio, video) included in the dataset is attributed to an individual author, indicated by initials and date embedded in the filename. The following initials refer to specific contributors (listed alphabetically): AM – Anna M. Maćkowiak; KB – Klaudia Bochniarz; LS – Łukasz Stypuła; MB – Maria Brzyska; MO – Mikhail Obzhigalin; MT – Monika Tarajko; NZ – Natalia Zawiejska; RP – Radosław Piskorski; SS – Sophie Stolberg; WW – Weronika Wolska. This naming convention ensures the traceability of authorship for each dataset element and reflects the collaborative and distributed nature of the data collection process. Descriptions and Names are authored by Natalia Zawiejska and Łukasz Stypuła. During the research process, contributors provided an initial description of the data (name, short description, ethnographic notes if necessary, additional information on the researched data if necessary). The final version of the data description is authored by Natalia Zawiejska and Łukasz Stypuła. Dorota Wąsik - proofreading of the database in English and translation of names from English into Polish (Kraków, Gdańsk). Grzegorz Słowiński - proofreading of the database in English and translation of names from English into Polish (Lublin). This data set constitutes the central component of the RUM PROJECT (Religion-Urbanity-Mapping), a research-based spatial archive of urban religion in Poland. It was developed as part of an interdisciplinary project conducted at the Faculty of Philosophy, Jagiellonian University in Kraków, involving scholars from fields such as religious studies, anthropology, human geography, urban sociology, and digital humanities. The RUM Archive comprises a complex and relational database documenting religious elements and practices in the urban space of three Polish cities: Kraków, Lublin, and Gdańsk. It is structured spatially and semantically through a typology of entries, points, lines, and streets, each connected to a set of metadata categories developed by the research team. These categories reflect an attempt to describe and contextualize religious icons and phenomena in urban space, without imposing hierarchical, historical, or confessional prioritization. The dataset includes thousands of entries with standardized information (e.g., religion, temporality, visibility, organizational structure, political/worldview relations, etc.), enriched by photographs, video recordings, sound files, and vector data (geodatabase). All content is georeferenced and linked to an interactive map built within the RUM Project Archive / Urban Religion Archive (URA) application. To ensure ethical data use, images of private individuals were blurred or taken from anonymizing angles, and sensitive content was either omitted or made imprecise in terms of geolocation. Public figures and entities appearing in the material remain visible as part of the documented urban landscape. All data were collected following approval by the Faculty Ethics Committee of the Jagiellonian University. The RUM dataset enables reproducible research, comparative studies, and interdisciplinary reflection on religion’s spatial, material, and social presence in the city. It was designed not only as an archive but also as a tool for theorizing urban religion in Central and Eastern Europe. The repository is accompanied by a research glossary (available as a separate file), a category system, and a user guide to interpret the data (README file). It complies with the FAIR principles and OpenAIRE guidelines, and each dataset is assigned a DOI to ensure citation and accessibility. Project was funded by the National Science Center in the OPUS 19 program no. 2020/37/B/HS1/02363; Agreement no. UMO-2020/37/B/HS1/02363.

A vector dataset containing bus stops, routes, and topology for 299 cities in China was generated and stored in Shapefile format. The dataset in mainland China (data for Taiwan Province is not available) is up to April 2024.

The original language of this dataset is Chinese, and we have used the Microsoft Translation API to uniformly translate the Chinese content into English.

For each city, the dataset contains two types of data: bus stops and bus routes (distinguishing direction, with one being undirected and the other being directed). The topological network dataset consists of nodes and edges, with the edges being undirected.

Law Enforcement Locations in Utah Any location where sworn officers of a law enforcement agency are regularly based or stationed. Law enforcement agencies "are publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers". This is the definition used by the US Department of Justice - Bureau of Justice Statistics (DOJ-BJS) for their Law Enforcement Management and Administrative Statistics (LEMAS) survey. Although LEMAS only includes non Federal Agencies, this dataset includes locations for federal, state, local, and special jurisdiction law enforcement agencies. Law enforcement agencies include, but are not limited to, municipal police, county sheriffs, state police, school police, park police, railroad police, federal law enforcement agencies, departments within non law enforcement federal agencies charged with law enforcement (e.g., US Postal Inspectors), and cross jurisdictional authorities (e.g., Port Authority Police). In general, the requirements and training for becoming a sworn law enforcement officer are set by each state. Law Enforcement agencies themselves are not chartered or licensed by their state. County, city, and other government authorities within each state are usually empowered by their state law to setup or disband Law Enforcement agencies. Generally, sworn Law Enforcement officers must report which agency they are employed by to the state. Although TGS's intention is to only include locations associated with agencies that meet the above definition, TGS has discovered a few locations that are associated with agencies that are not publicly funded. TGS is deleting these locations as we become aware of them, but some probably still exist in this dataset. Personal homes, administrative offices and temporary locations are intended to be excluded from this dataset, but a few may be included. Personal homes of constables may exist due to fact that many constables work out of their home. FBI entites are intended to be excluded from this dataset, but a few may be included. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] attribute. Based upon this attribute, the oldest record dates from 2006/06/27 and the newest record dates from 2013/05/20Last Update: March 6, 2014

🇺🇸 미국 English The datasets used in the creation of the predicted Habitat Suitability models includes the CWHR range maps of Californias regularly-occurring vertebrates which were digitized as GIS layers to support the predictions of the CWHR System software. These vector datasets of CWHR range maps are one component of California Wildlife Habitat Relationships (CWHR), a comprehensive information system and predictive model for Californias wildlife. The CWHR System was developed to support habitat conservation and management, land use planning, impact assessment, education, and research involving terrestrial vertebrates in California. CWHR contains information on life history, management status, geographic distribution, and habitat relationships for wildlife species known to occur regularly in California. Range maps represent the maximum, current geographic extent of each species within California. They were originally delineated at a scale of 1:5,000,000 by species-level experts and have gradually been revised at a scale of 1:1,000,000. For more information about CWHR, visit the CWHR webpage (https://www.wildlife.ca.gov/Data/CWHR). The webpage provides links to download CWHR data and user documents such as a look up table of available range maps including species code, species name, and range map revision history; a full set of CWHR GIS data; .pdf files of each range map or species life history accounts; and a User Guide.The models also used the CALFIRE-FRAP compiled "best available" land cover data known as Fveg. This compilation dataset was created as a single data layer, to support the various analyses required for the Forest and Rangeland Assessment, a legislatively mandated function. These data are being updated to support on-going analyses and to prepare for the next FRAP assessment in 2015. An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protections CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990 to 2014. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system.CWHR range data was used together with the FVEG vegetation maps and CWHR habitat suitability ranks to create Predicted Habitat Suitability maps for species. The Predicted Habitat Suitability maps show the mean habitat suitability score for the species, as defined in CWHR. CWHR defines habitat suitability as NO SUITABILITY (0), LOW (0.33), MEDIUM (0.66), or HIGH (1) for reproduction, cover, and feeding for each species in each habitat stage (habitat type, size, and density combination). The mean is the average of the reproduction, cover, and feeding scores, and can be interpreted as LOW (less than 0.34), MEDIUM (0.34-0.66), and HIGH (greater than 0.66) suitability. Note that habitat suitability ranks were developed based on habitat patch sizes >40 acres in size, and are best interpreted for habitat patches >200 acres in size. The CWHR Predicted Habitat Suitability rasters are named according to the 4 digit alpha-numeric species CWHR ID code. The CWHR Species Lookup Table contains a record for each species including its CWHR ID, scientific name, common name, and range map revision history (available for download at https://www.wildlife.ca.gov/Data/CWHR).

WFRC Community Focus Areas (2023)Geographic Representation Units WFRC’s Community Focus Areas (CFAs) are geographic areas for which additional consideration may be given within the planning and programming processes for future transportation, economic development, and other projects administered through WFRC. CFAs are used by WFRC in support of meeting the Council-established goal of promoting “inclusive engagement in transportation planning processes and equitable access to affordable and reliable transportation options.” CFAs are designated from Census block group geographic zones that meet the criteria described below. Census block groups are used as these are the smallest geographic areas for which more detailed household characteristics like employment, income, vehicle ownership, commute trip, and English language proficiency are available. WFRC recognizes the limitations of geography-based analysis, as proper planning work considers together the needs of individuals, groups and sectors, and geographic areas. However, geography-based analyses offer a useful starting point for the consideration and prioritization of projects that will serve specific community needs.2023 Community Focus Area Criteria UpdateFor the 2023 RTP planning cycle, WFRC will use two factors in designating geography-based CFAs: 1) concentration of low-income households and 2) concentration of persons identifying as members of racial and ethnic minority groups. The geography for these factors can be identified from consistent and regularly updated data sources maintained by the U.S. Census Bureau. WFRC will also make data available that conveys, while maintaining individual anonymity, the geographic distribution of additional measures including concentrations of persons with disabilities, households with limited English language proficiency, households that do not own a vehicle, older residents (65+ years of age), and younger residents (0-17 years of age). While the application of these factors within the planning process is less straightforward because of their higher statistical margins of error and comparatively even distribution within the region, these additional factors remain valuable as planning context. Low Income Focus Areas, Methodology for IdentificationThe block group-level data from the 2020 Census American Community Survey (ACS) 5-year dataset (Table C17002: Ratio of Income to Poverty Level), is used to determine the percentage of the population within each block group that are in households that have a ratio of income to federal poverty threshold of equal to or less than 1, i.e., their income is below the poverty level. The federal poverty threshold is set differently for households, considering their household size and age of household members.Census block groups in which more than 20% of the households whose income is less than or equal to the federal poverty threshold are included in the WFRC CFAs and designated as Low-Income focus areas. Racial and Ethnic Minority Focus AreasThe block group-level data from the 2020 ACS 5-year dataset (Table B03002: Hispanic or Latino Origin By Race) is used to determine the percentage of the population that did not self-identify their race and ethnicity as “White alone.” The average census block group area in the Wasatch Front urbanized areas has 24.2% of its population that identifies as Black or African American alone, American Indian, and Alaska Native alone, Asian alone, Native Hawaiian and other Pacific Islander alone, some other race alone, two or more races, or of Hispanic or Latino origin.Census blocks in which more than 40%2 of the population identifies as one or more of the racial or ethnic groups listed above are included in the WFRC CFAs and designated as Racial and Ethnic Minority focus areas.Excluding Predominantly Non-Residential Areas from CFAsSome census block groups that meet one or both of the CFA criteria described above contain large, non-residential areas or low density residential areas. Such census block areas may have small residential neighborhoods surrounded by predominantly commercial or industrial land uses, or large areas of public land or as-yet undeveloped lands. For this reason, WFRC staff may adjust the boundaries of an CFA whose census block group population density is less than 500 persons per square mile, to exclude areas of those block groups that have large, predominantly non-residential land uses.Community Focus Area Update FrequencyThe geography for WFRC CFAs will be updated not less than every four years, preceding the project phasing period of the Regional Transportation Planning update cycle. The update will use the most recent version of the 5 year ACS dataset. The next update is expected in the summer of 2026 (the beginning of the 4th year for the 2027 RTP development process) and is expected to use the 2024 5-year ACS results that average results across 2020-2024.Footnotes:1. The 2019 version of WFRC CFAs used ‘Zero Car Households’ as a third factor. This factor is no longer included because of its geographic and statistical fluctuation over time in data reported by the American Community Survey. Additionally, ‘Zero Car households’ was observed to have a strong relationship with the other two CFA designation factors.2. The percentage threshold specified here is approximately one standard deviation above the regional mean for this indicator. Assuming a statistically normal distribution, approximately 16% of the overall set (i.e. census blocks, in this case) would fall above a one standard deviation threshold.3. Table B03002 includes information from both 'Race' and 'Hispanic or Latino Origin' identification questions asked as part of the Census Bureau's American Community Survey.

The objective of the priority habitat map in England is to:• help organisations protect the most natural remaining examples of rivers from further impacts on natural processes, and • highlight any aspects of habitat integrity (hydrological, chemical, physical, biological) that could most usefully be improved. The priority river habitat map that has been produced is an English interpretation of the UK definition of priority river habitat, focusing on naturalness as the principal criterion in recognition of the vital importance of natural processes in delivering sustainable riverine habitats and supporting characteristic biodiversity.Full metadata can be viewed on data.gov.uk.

The Ontario GeoHub Item Report contains details on the items present in Ontario GeoHub and CarrefourGéo Ontario. The report can be filtered using the "date of last data update" to find recently updated items. See below for a list of the fields and descriptions.

Status

On going: data is being continually updated

Maintenance and Update Frequency

Daily: data is updated each day

Contact

Land Information Ontario Support, lio@ontario.ca

Data Dictionary

    dataset_id - Identifier assigned to the item by ArcGIS Hub. For items based on a feature service this is a combination of the ArcGIS Online id and the layer number of the feature layer in the associated service. Format [agol_id]_[layer number].

    item_id - ArcGIS Online identifier associated with the item

    slug - Easy-to-read identifier for the dataset used in URLs

    url_dataset_id - Complete URL to the item using the dataset_id

    url_slug - Complete URL to the item using the slug

    item_tile - Item title

    snippet - Short description of the item

    item_type - ArcGIS Hub item type

    site - The site that the item resides in. Possible values: 'Ontario GeoHub' or 'CarrefourGéo Ontario'

    metadata_lang - Language of the metadata based on mdLang tag in metadata record

    detected_lang - Language of the metadata based on detection of the title and description using Google Translate libraries

    tags - ArcGIS Online tags associated with the item

    grp_name - Name of the Open Data Group through which this item was shared to ArcGIS Hub

    grp_id - Identifier of the Open Data Group through which this item was shared to ArcGIS Hub

    grp_owner - Name of the ArcGIS Online organization that owns the Open Data Group

    dataset_id_eng - The dataset_id of the related English record in Ontario GeoHub. Only applies to records where site = 'CarrefourGéo Ontario'

    dataset_id_fre - The dataset_id of the the related French record in CarrefourGeo Ontario. Only applied to records where site = 'Ontario GeoHub'
  item_id_eng - The item_id of the related English record in Ontario GeoHub. Only applies to records where site = 'CarrefourGéo Ontario'
  item_id_fre - The item_id of the the related French record in CarrefourGeo Ontario. Only applied to records where site = 'Ontario GeoHub'

    legacy_id - Identifier of the source record in the legacy Metadata Management Tool, where applicable

    ccsn - LIO Concrete Class Short Name associated with the item, where applicable

    agol_owner - Username of the ArcGIS Online user that owns the item

    agol_org - Name of the ArcGIS Organization to which the item belongs

    publisher - Item "source" organization as displayed in Hub search results

    publisher_src - Location from which Hub pulled the value of publisher

    data_url - The data url associated with the item

    fgdb_link - Link to the LIO-generated file geodatabase download package associated with the item, where applicable

    shp_link - Link to the LIO-generated shapefile download package associated with the item, where applicable

    created_dt - Item creation date

    modified_dt - Item modified date

    dl_package_dt - Esri download creation date

    dl_lastrety_dt - Date of last attempt to generate Esri download

    data_currency_dt - Date of last data update

    data_currency_dt_src - Source from which data_currency_dt was retrieved

    metadata_present - Indicates whether an ISO-19115 NAP metadata record exists for this item

    metadata_url - Direct URL to the unformatted XML metadata for the item