Socio-Economic Index of 7 variables overlayed to compare with the physical blight index- Education, Median Household Income, Renter Occupied, Single Parent Households, Population Density, Poverty Rate, and Unemployment Rate. This map was used to help question what socio-economic factors correlate with the observance of blighted areas in order to better create strategic decisions on how to best prevent blight.By using this dataset you acknowledge the following:Kansas Open Records Act StatementThe Kansas Open Records Act provides in K.S.A. 45-230 that "no person shall knowingly sell, give or receive, for the purpose of selling or offering for sale, any property or service to persons listed therein, any list of names and addresses contained in, or derived from public records..." Violation of this law may subject the violator to a civil penalty of $500.00 for each violation. Violators will be reported for prosecution.By accessing this site, the user makes the following certification pursuant to K.S.A. 45-220(c)(2): "The requester does not intend to, and will not: (A) Use any list of names or addresses contained in or derived from the records or information for the purpose of selling or offering for sale any property or service to any person listed or to any person who resides at any address listed; or (B) sell, give or otherwise make available to any person any list of names or addresses contained in or derived from the records or information for the purpose of allowing that person to sell or offer for sale any property or service to any person listed or to any person who resides at any address listed."

Terms of Use:

Data Limitations Disclaimer

The MassDEP Estimated Sewer System Service Area Boundaries datalayer may not be complete, may contain errors, omissions, and other inaccuracies, and the data are subject to change. The user’s use of and/or reliance on the information contained in the Document (e.g. data) shall be at the user’s own risk and expense. MassDEP disclaims any responsibility for any loss or harm that may result to the user of this data or to any other person due to the user’s use of the Document.

All sewer service area delineations are estimates for broad planning purposes and should only be used as a guide. The data is not appropriate for site-specific or parcel-specific analysis. Not all properties within a sewer service area are necessarily served by the system, and some properties outside the mapped service areas could be served by the wastewater utility – please contact the relevant wastewater system. Not all service areas have been confirmed by the sewer system authorities.

This is an ongoing data development project. Attempts have been made to contact all sewer/wastewater systems, but not all have responded with information on their service area. MassDEP will continue to collect and verify this information. Some sewer service areas included in this datalayer have not been verified by the POTWs, privately-owned treatment works, GWDPs, or the municipality involved, but since many of those areas are based on information published online by the municipality, the utility, or in a publicly available report, they are included in the estimated sewer service area datalayer.

Please use the following citation to reference these data

MassDEP. Water Utility Resilience Program. 2025. Publicly-Owned Treatment Work and Non-Publicly-Owned Sewer Service Areas (PubV2024_12).

We want to learn about the data uses. If you use this dataset, please notify staff in the Water Resilience program (WURP@mass.gov).

Layers and Tables:

The MassDEP Estimated Sewer System Service Area data layer comprises two feature classes and a supporting table:

Publicly-Owned Treatment Works (POTW) Sewer Service Areas feature class SEWER_SERVICE_AREA_POTW_POLY includes polygon features for sewer service areas systems operated by publicly owned treatment works (POTWs)Non-Publicly Owned Treatment Works (NON-POTW) Sewer Service Areas feature class SEWER_SERVICE_AREA_NONPOTW_POLY includes polygon features for sewer service areas for operated by NON publicly owned treatment works (NON-POTWs)The Sewer Service Areas Unlocated Sites table SEWER_SERVICE_AREA_USL contains a list of known, unmapped active POTW and NON-POTW services areas at the time of publication.

ProductionData Universe

Effluent wastewater treatment plants in Massachusetts are permitted either through the Environmental Protection Agency’s (EPA) National Pollutant Discharge Elimination System (NPDES) surface water discharge permit program or the MassDEP Groundwater Discharge Permit Program. The WURP has delineated active service areas served by publicly and privately-owned effluent treatment works with a NPDES permit or a groundwater discharge permit.

National Pollutant Discharge Elimination System (NPDES) Permits

In the Commonwealth of Massachusetts, the EPA is the permitting authority for regulating point sources that discharge pollutants to surface waters. NPDES permits regulate wastewater discharge by limiting the quantities of pollutants to be discharged and imposing monitoring requirements and other conditions. NPDES permits are typically co-issued by EPA and the MassDEP. The limits and/or requirements in the permit ensure compliance with the Massachusetts Surface Water Quality Standards and Federal Regulations to protect public health and the aquatic environment. Areas served by effluent treatment plants with an active NPDES permit are included in this datalayer based on a master list developed by MassDEP using information sourced from the EPA’s Integrated Compliance Information System (ICIS).

Groundwater Discharge (GWD) Permits

In addition to surface water permittees, the WURP has delineated all active systems served by publicly and privately owned effluent treatment works with groundwater discharge (GWD) permits, and some inactive service areas. Groundwater discharge permits are required for systems discharging over 10,000 GPD sanitary wastewater – these include effluent treatment systems for public, district, or privately owned effluent treatment systems. Areas served by an effluent treatment plant with an active GWD permit are included in this datalayer based on lists received from MassDEP Wastewater staff.

Creation of Unique IDs for Each Service Area

The Sewer Service Area datalayer contains polygons that represent the service area of a particular wastewater system within a particular municipality. Every discharge permittee is assigned a unique NPDES permit number by EPA or a unique GWD permit identifier by MassDEP. MassDEP WURP creates a unique Sewer_ID for each service area by combining the municipal name of the municipality served with the permit number (NPDES or GWD) ascribed to the sewer that is serving that area. Some municipalities contain more than one sewer system, but each sewer system has a unique Sewer_ID. Occasionally the area served by a sewer system will overlap another town by a small amount – these small areas are generally not given a unique ID. The Estimated sewer Service Area datalayer, therefore, contains polygons with a unique Sewer_ID for each sewer service area. In addition, some municipalities will have multiple service areas being served by the same treatment plant – the Sewer_ID for these will contain additional identification, such as the name of the system, to uniquely identify each system.

Classifying System Service Areas

WURP staff reviewed the service areas for each system and, based on OWNER_TYPE, classified as either a publicly-owned treatment work (POTW) or a NON-POTW (see FAC_TYPE field). Each service area is further classified based on the population type served (see SECTOR field).

Methodologies and Data Sources

Several methodologies were used to create service area boundaries using various sources, including data received from the sewer system in response to requests for information from the MassDEP WURP project, information on file at MassDEP, and service area maps found online at municipal and wastewater system websites. When MassDEP received sewer line data rather than generalized areas, 300-foot buffers were created around the sewer lines to denote service areas and then edited to incorporate generalizations. Some municipalities submitted parcel data or address information to be used in delineating service areas. Many of the smaller GWD permitted sewer service areas were delineated using parcel boundaries related to the address on file.

Verification Process

Small-scale pdf file maps with roads and other infrastructure were sent to systems for corrections or verifications. If the system were small, such as a condominium complex or residential school, the relevant parcels were often used as the basis for the delineated service area. In towns where 97% or more of their population is served by the wastewater system and no other service area delineation was available, the town boundary was used as the service area boundary. Some towns responded to the request for information or verification of service areas by stating that the town boundary should be used since all, or nearly all, of the municipality is served by one wastewater system.

To ensure active systems are mapped, WURP staff developed two work flows. For NPDES-permitted systems, WURP staff reviewed available information on EPA’s ICIS database and created a master list of these systems. Staff will work to routinely update this master list by reviewing the ICIS database for new NPDES permits. The master list will serve as a method for identifying active systems, inactive systems, and unmapped systems. For GWD permittees, GIS staff established a direct linkage to the groundwater database, which allows for populating information into data fields and identifying active systems, inactive systems, and unmapped systems.

All unmapped systems are added to the Sewer Service Area Unlocated List (SEWER_SERVICE_AREAS_USL) for future mapping. Some service areas have not been mapped but their general location is represented by a small circle which serves as a placeholder - the location of these circles are estimated based on the general location of the treatment plant or the general estimated location of the service area - these do not represent the actual service area.

Sources of information for estimated wastewater service areas:

EEOA Water Assets Project (2005) sewer lines (these were buffered to create service areas) Horsely Witten Report 2008 Municipal Master Plans, Open Space Plans, Facilities Plans, Wastewater and Sewer System Webpages, reports and online interactive maps GIS data received from POTWs and NON-POTWs Detailed infrastructure mapping completed through the MassDEP WURP Critical Infrastructure Initiative

In the absence of other service area information, for municipalities served by a town-wide sewer system serving at least 97% of the population, the municipality’s boundary was used. Percent served information and determinations of which municipalities are 97% or more served by the wastewater system were made based on the Percent Sewer Service Map created in 2018 by MassDEP based on various sources of information including but not limited to:

The number of services as a percent of developed parcelsTaken directly from a Master Plan, Sewer Department Website, Open Space Plan, etc. found online Calculated using information from the town on population served MassDEP staff estimateHorsely Witten Report 2008 or Pioneer Institute 2004 Calculated from Sewer System Areas Mapped through MassDEP WURP Critical

Abstract

A crosswalk dataset matching US ZIP codes to corresponding census tracts

Documentation

The denominators used to calculate the address ratios are the ZIP code totals. When a ZIP is split by any of the other geographies, that ZIP code is duplicated in the crosswalk file.

**Example: **ZIP code 03870 is split by two different Census tracts, 33015066000 and 33015071000, which appear in the tract column. The ratio of residential addresses in the first ZIP-Tract record to the total number of residential addresses in the ZIP code is .0042 (.42%). The remaining residential addresses in that ZIP (99.58%) fall into the second ZIP-Tract record.

So, for example, if one wanted to allocate data from ZIP code 03870 to each Census tract located in that ZIP code, one would multiply the number of observations in the ZIP code by the residential ratio for each tract associated with that ZIP code.

https://redivis.com/fileUploads/4ecb405e-f533-4a5b-8286-11e56bb93368%3E" alt="">(Note that the sum of each ratio column for each distinct ZIP code may not always equal 1.00 (or 100%) due to rounding issues.)

Census tract definition

A census tract, census area, census district or meshblock is a geographic region defined for the purpose of taking a census. Sometimes these coincide with the limits of cities, towns or other administrative areas and several tracts commonly exist within a county. In unincorporated areas of the United States these are often arbitrary, except for coinciding with political lines.

Further reading

The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. URL: https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf

Contact information

Questions regarding these crosswalk files can be directed to Alex Din with the subject line HUD-Crosswalks.

Acknowledgement

This dataset is taken from the U.S. Department of Housing and Urban Development (HUD) office: https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook

This data layer is an element of the Oregon GIS Framework. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

A. SUMMARY The Department of Public Health and the Mayor’s Office of Housing and Community Development, with support from the Planning Department, created these 41 neighborhoods by grouping 2010 Census tracts, using common real estate and residents’ definitions for the purpose of providing consistency in the analysis and reporting of socio-economic, demographic, and environmental data, and data on City-funded programs and services. These neighborhoods are not codified in Planning Code nor Administrative Code, although this map is referenced in Planning Code Section 415 as the “American Community Survey Neighborhood Profile Boundaries Map. Note: These are NOT statistical boundaries as they are not controlled for population size. This is also NOT an official map of neighborhood boundaries in SF but an aggregation of Census tracts and should be used in conjunction with other spatial boundaries for decision making. B. HOW THE DATASET IS CREATED This dataset is produced by assigning Census tracts to neighborhoods based on existing neighborhood definitions used by Planning and MOHCD. A qualitative assessment is made to identify the appropriate neighborhood for a given tract based on understanding of population distribution and significant landmarks. Once all tracts have been assigned a neighborhood, the tracts are dissolved to produce this dataset, Analysis Neighborhoods. C. UPDATE PROCESS This dataset is static. Changes to the analysis neighborhood boundaries will be evaluated as needed by the Analysis Neighborhood working group led by DataSF and the Planning department and includes staff from various other city departments. Contact us for any questions. D. HOW TO USE THIS DATASET Downloading this dataset and opening it in Excel may cause some of the data values to be lost or not display properly (particularly the Analysis Neighborhood column). For a simple list of Analysis Neighborhoods without geographic coordinates, click here: https://data.sfgov.org/resource/xfcw-9evu.csv?$select=nhood E. RELATED DATASETS 2020 Census tracts assigned a neighborhood 2010 Census tracts assigned a neighborhood

This site provides access to download an ArcGIS geodatabase or shapefiles for the 2017 Texas Address Database, compiled by the Center for Water and the Environment (CWE) at the University of Texas at Austin, with guidance and funding from the Texas Division of Emergency Management (TDEM). These addresses are used by TDEM to help anticipate potential impacts of serious weather and flooding events statewide. This is part of the Texas Water Model (TWM), a project to adapt the NOAA National Water Model [1] for use in Texas public safety. This database was compiled over the period from June 2016 to December 2017. A number of gaps remain (towns and cities missing address points), see Address Database Gaps spreadsheet below [4]. Additional datasets include administrative boundaries for Texas counties (including Federal and State disaster-declarations), Councils of Government, and Texas Dept of Public Safety Regions. An Esri ArcGIS Story Map [5] web app provides an interactive map-based portal to explore and access these data layers for download.

The address points in this database include their "height above nearest drainage" (HAND) as attributes in meters and feet. HAND is an elevation model developed through processing by the TauDEM method [2], built on USGS National Elevation Data (NED) with 10m horizontal resolution. The HAND elevation data and 10m NED for the continental United States are available for download from the Texas Advanced Computational Center (TACC) [3].

The complete statewide dataset contains about 9.28 million address points representing a population of about 28 million. The total file size is about 5GB in shapefile format. For better download performance, the shapefile version of this data is divided into 5 regions, based on groupings of major watersheds identified by their hydrologic unit codes (HUC). These are zipped by region, with no zipfile greater than 120mb: - North Tx: HUC1108-1114 (0.52 million address points) - DFW-East Tx: HUC1201-1203 (3.06 million address points) - Houston-SE Tx: HUC1204 (1.84 million address points) - Central Tx: HUC1205-1210 (2.96 million address points) - Rio Grande-SW Tx: HUC2111-1309 (2.96 million address points)

Additional state and county boundaries are included (Louisiana, Mississippi, Arkansas), as well as disaster-declaration status.

Compilation notes: The Texas Commission for State Emergency Communications (CSEC) provided the first 3 million address points received, in a single batch representing 213 of Texas' 254 counties. The remaining 41 counties were primarily urban areas comprising about 6.28 million addresses (totaling about 9.28 million addresses statewide). We reached the GIS data providers for these areas (see Contributors list below) through these emergency communications networks: Texas 9-1-1 Alliance, the Texas Emergency GIS Response Team (EGRT), and the Texas GIS 9-1-1 User Group. The address data was typically organized in groupings of counties called Councils of Governments (COG) or Regional Planning Commissions (RPC) or Development Councils (DC). Every county in Texas belongs to a COG, RPC or DC. We reconciled all counties' addresses to a common, very simple schema, and merged into a single geodatabase.

November 2023 updates: In 2019, TNRIS took over maintenance of the Texas Address Database, which is now a StratMap program updated annually [6]. In 2023, TNRIS also changed its name to the Texas Geographic Information Office (TxGIO). The datasets available for download below are not being updated, but are current as of the time of Hurricane Harvey.

References: [1] NOAA National Water Model [https://water.noaa.gov/map] [2] TauDEM Downloads [https://hydrology.usu.edu/taudem/taudem5/downloads.html] [3] NFIE Continental Flood Inundation Mapping - Data Repository [https://web.corral.tacc.utexas.edu/nfiedata/] [4] Address Database Gaps, Dec 2017 (download spreadsheet below) [5] Texas Address and Base Layers Story Map [https://www.hydroshare.org/resource/6d5c7dbe0762413fbe6d7a39e4ba1986/] [6] TNRIS/TxGIO StratMap Address Points data downloads [https://tnris.org/stratmap/address-points/]

Abstract

The National Family Health Survey (NFHS) was carried out as the principal activity of a collaborative project to strengthen the research capabilities of the Population Reasearch Centres (PRCs) in India, initiated by the Ministry of Health and Family Welfare (MOHFW), Government of India, and coordinated by the International Institute for Population Sciences (IIPS), Bombay. Interviews were conducted with a nationally representative sample of 89,777 ever-married women in the age group 13-49, from 24 states and the National Capital Territoty of Delhi. The main objective of the survey was to collect reliable and up-to-date information on fertility, family planning, mortality, and maternal and child health. Data collection was carried out in three phases from April 1992 to September 1993. THe NFHS is one of the most complete surveys of its kind ever conducted in India.

The households covered in the survey included 500,492 residents. The young age structure of the population highlights the momentum of the future population growth of the country; 38 percent of household residents are under age 15, with their reproductive years still in the future. Persons age 60 or older constitute 8 percent of the population. The population sex ratio of the de jure residents is 944 females per 1,000 males, which is slightly higher than sex ratio of 927 observed in the 1991 Census.

The primary objective of the NFHS is to provide national-level and state-level data on fertility, nuptiality, family size preferences, knowledge and practice of family planning, the potentiel demand for contraception, the level of unwanted fertility, utilization of antenatal services, breastfeeding and food supplemation practises, child nutrition and health, immunizations, and infant and child mortality. The NFHS is also designed to explore the demographic and socioeconomic determinants of fertility, family planning, and maternal and child health. This information is intended to assist policymakers, adminitrators and researchers in assessing and evaluating population and family welfare programmes and strategies. The NFHS used uniform questionnaires and uniform methods of sampling, data collection and analysis with the primary objective of providing a source of demographic and health data for interstate comparisons. The data collected in the NFHS are also comparable with those of the Demographic and Health Surveys (DHS) conducted in many other countries.

Geographic coverage

National

Analysis unit

• Household
• Data collected for women 13-49, indicators calculated for women 15-49

Universe

The population covered by the 1992-93 DHS is defined as the universe of all women age 13-49 who were either permanent residents of the households in the NDHS sample or visitors present in the households on the night before the survey were eligible to be interviewed.

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The sample design for the NFHS was discussed during a Sample Design Workshop held in Madurai in Octber, 1991. The workshop was attended by representative from the PRCs; the COs; the Office of the Registrar General, India; IIPS and the East-West Center/Macro International. A uniform sample design was adopted in all the NFHS states. The Sample design adopted in each state is a systematic, stratified sample of households, with two stages in rural areas and three stages in urban areas.

SAMPLE SIZE AND ALLOCATION

The sample size for each state was specified in terms of a target number of completed interviews with eligible women. The target sample size was set considering the size of the state, the time and ressources available for the survey and the need for separate estimates for urban and rural areas of the stat. The initial target sample size was 3,000 completed interviews with eligible women for states having a population of 25 million or less in 1991; 4,000 completed interviews for large states with more than 25 million population; 8,000 for Uttar Pradesh, the largest state; and 1,000 each for the six small northeastern states. In States with a substantial number of backward districts, the initial target samples were increased so as to allow separate estimates to be made for groups of backward districts.

The urban and rural samples within states were drawn separetly and , to the extent possible, sample allocation was proportional to the size of the urban-rural populations (to facilitate the selection of a self-weighting sample for each state). In states where the urban population was not sufficiently large to provide a sample of at least 1,000 completed interviews with eligible women, the urban areas were appropriately oversampled (except in the six small northeastern states).

THE RURAL SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A two-stage stratified sampling was adopted for the rural areas: selection of villages followed by selection of households. Because the 1991 Census data were not available at the time of sample selection in most states, the 1981 Census list of villages served as the sampling frame in all the states with the exception of Assam, Delhi and Punjab. In these three states the 1991 Census data were used as the sampling frame.

Villages were stratified prior to selection on the basis of a number of variables. The firts level of stratification in all the states was geographic, with districts subdivided into regions according to their geophysical characteristics. Within each of these regions, villages were further stratified using some of the following variables : village size, distance from the nearest town, proportion of nonagricultural workers, proportion of the population belonging to scheduled castes/scheduled tribes, and female literacy. However, not all variables were used in every state. Each state was examined individually and two or three variables were selected for stratification, with the aim of creating not more than 12 strata for small states and not more than 15 strata for large states. Females literacy was often used for implicit stratification (i.e., the villages were ordered prior to selection according to the proportion of females who were literate). Primary sampling Units (PSUs) were selected systematically, with probaility proportional to size (PPS). In some cases, adjacent villages with small population sizes were combined into a single PSU for the purpose of sample selection. On average, 30 households were selected for interviewing in each selected PSU.

In every state, all the households in the selected PSUs were listed about two weeks prior to the survey. This listing provided the necessary frame for selecting households at the second sampling stage. The household listing operation consisted of preparing up-to-date notional and layout sketch maps of each selected PSU, assigning numbers to structures, recording addresses (or locations) of these structures, identifying the residential structures, and listing the names of the heads of all the households in the residentiak structures in the selected PSU. Each household listing team consisted of a lister and a mapper. The listing operation was supervised by the senior field staff of the concerned CO and the PRC in each state. Special efforts were made not to miss any household in the selected PSU during the listing operation. In PSUs with fewer than 500 households, a complete household listing was done. In PSUs with 500 or more households, segmentation of the PSU was done on the basis of existing wards in the PSU, and two segments were selected using either systematic sampling or PPS sampling. The household listing in such PSUs was carried out in the selected segments. The households to be interviewed were selected from provided with the original household listing, layout sketch map and the household sample selected for each PSU. All the selected households were approached during the data collection, and no substitution of a household was allowed under any circumstances.

THE RURAL URBAN SAMPLE: THE FRAME, STRATIFICATION AND SELECTION

A three-stage sample design was adopted for the urban areas in each state: selection of cities/towns, followed by urban blocks, and finally households. Cities and towns were selected using the 1991 population figures while urban blocks were selected using the 1991 list of census enumeration blocks in all the states with the exception of the firts phase states. For the first phase states, the list of urban blocks provided by the National Sample Survey Organization (NSSSO) served as the sampling frame.

All cities and towns were subdivided into three strata: (1) self-selecting cities (i.e., cities with a population large enough to be selected with certainty), (2) towns that are district headquaters, and (3) other towns. Within each stratum, the cities/towns were arranged according to the same kind of geographic stratification used in the rural areas. In self-selecting cities, the sample was selected according to a two-stage sample design: selection of the required number of urban blocks, followed by selection of households in each of selected blocks. For district headquarters and other towns, a three stage sample design was used: selection of towns with PPS, followed by selection of two census blocks per selected town, followed by selection of households from each selected block. As in rural areas, a household listing was carried out in the selected blocks, and an average of 20 households per block was selected systematically.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the NFHS: the Household Questionnaire, the Women's Questionnaire, and the Village Questionnaire. The overall content

List of United States Postal Service (USPS) Zone Improvement Plan (ZIP) Codes found within or partially within the borders of the City of Detroit.

The data set was created by preparing fine-scale population-specific Species Distribution Models (SDMs) to map revised PHMA and GHMA areas for each of the six greater sage-grouse populations within the current occupied range of Colorado. First, known presence locations of marked greater sage-grouse were used to train Random Forest and Resource Selection Function (RSF) models to estimate seasonal (e.g., breeding, summer-fall and winter) habitat suitability. Secondly, the seasonal model results were classified into high or low habitat suitability categories and subsequently compiled to produce a year-round habitat suitability map. Third, the resulting year-round habitat suitability maps were used to develop revised PHMA and GHMA areas for each population. Finally, the current occupied range for each population were modified to 1) exclude areas identified as unsuitable habitats and 2) include areas outside of current occupied range where evidence of sage-grouse occupancy exists.Data inputs into the RSF and Random Forest Models included presence data from GPS and VHF collar data provided to Olsson from CPW biologists, which was used to refine the models. A combination of vegetative and topographic predictors were employed at multiple scales in assessing the probability of habitat selection for the populations analyzed in this study. The predictors were analyzed at multiple spatial scales, as the literature demonstrates that habitat selection by a species occurs at some scales and not others (Mayor et al. 2009, Acker et al. 2017). The predictors were measured at five scales: 100 meters (m), 400 m, 1000 m, 1600 m, and 3200 m. These were selected to assess a range of local- to landscape-level scales that may influence habitat selection. Furthermore, these scales are comparable to scales assessed in other contemporary studies concerning habitat selection of greater sage-grouse (Doherty et al. 2010; Rice et al. 2016; Walker et al. 2016).Populations were also analyzed to assess utilization of smaller mapped aspen stands as compared to larger continuous forested stands of aspen and/or mixed-conifer. While greater-sage grouse tend to avoid larger forested areas, they will utilize smaller aspen stands (T. Apa pers. comm. 2016-2018). All presence locations for each population were sampled against mapped aspen stands to calculate 1) the rate of selection for aspen stands by the population, and 2) the acreage of each aspen stand utilized. The sampled stand acreages were subsequently graphed and examined to identify natural breaks in the data. Stands with acreages less than the natural break value and not directly adjacent to other forested stands were classified and analyzed separately as isolated aspen polygons which were included as potentially suitable habitat; the remaining aspen stands were classified as forested and integrated with mixed-conifer forests, which were assumed to be non-suitable habitat.Finally, the distance to forested areas was measured as a vegetative predictor using the Euclidean Distance tool in ArcGIS 10.4, excluding all isolated aspen patches and mixed-conifer patches less than 0.5 acres (and see previous paragraph).Vegetation types were derived from the Colorado Vegetation Classification Project (CVCP), a 25 m resolution raster dataset developed by CPW, which mapped landcover conditions through the periods from 1993to 1997. In addition, vegetation types were also derived from the 2001 LANDFIRE Existing Vegetation Type (EVT) layer for areas adjacent to the study area in Utah and Wyoming to provide complete and continuous vegetation cover for populations abutting the state boundary. The LANDFIRE EVT is a 30 m resolution raster dataset developed by the United States Geological Survey (USGS) mapping landcover conditions from 2001 (LANDFIRE 2001). Vegetative types were classified into biologically relevant classes and subsequently measured as percent-proportion by dividing the number of cells for the particular class by the total number of cells within the radii of the five defined scales using ArcGIS 10.4. The assigned classes of vegetative types varied by population and are detailed in the population-specific reports provided to BLM.Topographic predictors were derived from the 10 m resolution National Elevation Dataset (NED) Digital Elevation Model (DEM) developed and maintained by the USGS. Key topographic predictors include aspect, Compound Topographic Index (CTI), elevation, percent slope, slope position and surface roughness. Aspect and percent slope were calculated in ArcGIS 10.4. CTI, slope position and surface roughness were calculated using the Geomorphology and Gradient Metrics toolbox (Evans et al. 2014). In addition, aspect was subsequently transformed using the TRASP method in the Geomorphology and Gradient Metrics toolbox. To develop the multi-scale predictors, CTI and percent slope were measured as the mean of all values within the radii of the five defined scales; slope position and surface roughness were calculated using the radii of the five defined scales.The following summary of the step-wise procedure was developed to convert the Random Forest and RSF continuous surface model results into revised Habitat Management Area Prescriptions. Details of these methods follow this list:1. Classify all seasonal Random Forest and RSF model results into high and low habitat suitability layers.2. Ensemble all Random Forest and RSF classified seasonal layers to form a single year-round annual habitat layer designating locations as either high or low habitat suitability.3. Convert all highly suitable locations to Priority Habitat Management Areas (PHMA) and all locations designated as low habitat suitability to General Habitat Management Areas (GHMA).4. Classify all areas within a 0.6-mile radius from lek locations having an active or unknown status designation as PHMA, regardless of habitat suitability classification.5. Identify all irrigated agricultural lands and designate interiors as Undesignated Habitat (UDH).6. Review and apply site-specific manual conversions of initial management prescription designations based on CPW biologist and stakeholder input.7. Remove identified non-habitat areas from Current Occupied Range (COR). Expand COR in areas beyond the current population boundary where evidence exists to demonstrate occupation by greater sage-grouse.The previous habitat layer generated by CPW, only two habitat designations prescribed by the BLM ARMPA exist for assigning management approaches for conservation of the Colorado greater sage-grouse populations; PHMA and GHMA. PHMA have the highest conservation value based on a combination of habitat and sage-grouse population characteristics and are managed to minimize disturbance activities through No Surface Occupancy (NSO) stipulations and implementing capped disturbance allowances. GHMA represent areas with lower greater sage-grouse occupancy and generally have marginal habitat conditions with fewer management restrictions that provide greater flexibility in land use activities.The initial step to applying PHMA and GHMA habitat management prescriptions involves converting all areas classified as highly suitable habitat in the population’s year-round classified habitat layer to PHMA, while the remaining low habitat suitability areas are converted to GHMA. Secondly, all lek locations with a CPW-prescribed active or unknown status designation are buffered with a 0.6-mile radius and the entirety of the interior of the buffer area is converted to PHMA. Third, the most recent mapped irrigated agricultural lands data was acquired from the Colorado Division of Water Resources for all applicable populations, then the following procedure described below were implemented to apply the Undesignated Habitat prescription to the interior of all irrigated agricultural lands.Undesignated HabitatThrough the course of this study, an additional management prescription was established by AGNC to address concerns regarding habitat management on privately held irrigated agricultural lands.An Undesignated Habitat(UDH) management prescription was developed to address concerns surrounding the management of privately held irrigated agricultural lands. The UDH prescription is applicable to all populations, excluding the Parachute-Piceance-Roan population (due to a lack of irrigated agricultural lands). UDH are areas of seasonally irrigated and harvested hay fields. These areas are utilized seasonally by sage-grouse, primarily in the late summer and fall, near edges where irrigated fields are adjacent and abutting sagebrush habitats. UDH is considered effective habitat, but it is the long-term irrigation and haying practices which have created and maintain this habitat type, and thus the unimpeded irrigation, haying operations and maintenance are not considered to be a negative impact to sage-grouse. While utilization of the edges of irrigated agricultural lands by sage-grouse is known to vary from population to population, studying grouse utilization on a population-specific basis proved problematic as most populations lacked adequate telemetry locations within irrigated agricultural lands to yield results with any level of confidence. For this reason, the North Park population was selected to analyze in detail due to the high number of telemetry points located within irrigated agricultural lands. Approximately 20 percent of all summer-fall telemetry locations for the North Park population occur within irrigated agricultural lands, compared to less than 1 percent to 3 percent utilization demonstrated in the remaining populations.All summer-fall telemetry locations occurring within irrigated agricultural lands were sampled to calculate the distance each point occurred from the edges of irrigated fields. The distances for each location were plotted in a histogram and subsequently reviewed by CPW and AGNC team consultants, revealing a natural break occurring in the

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. ZIP Code Tabulation Areas (ZCTAs) are approximate area representations of U.S. Postal Service (USPS) ZIP Code service areas that the Census Bureau creates to present statistical data for each decennial census. The Census Bureau delineates ZCTA boundaries for the United States, Puerto Rico, American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands once each decade following the decennial census. Data users should not use ZCTAs to identify the official USPS ZIP Code for mail delivery. The USPS makes periodic changes to ZIP Codes to support more efficient mail delivery. The Census Bureau uses tabulation blocks as the basis for defining each ZCTA. Tabulation blocks are assigned to a ZCTA based on the most frequently occurring ZIP Code for the addresses contained within that block. The most frequently occurring ZIP Code also becomes the five-digit numeric code of the ZCTA. These codes may contain leading zeros. Blocks that do not contain addresses but are surrounded by a single ZCTA (enclaves) are assigned to the surrounding ZCTA. Because the Census Bureau only uses the most frequently occurring ZIP Code to assign blocks, a ZCTA may not exist for every USPS ZIP Code. Some ZIP Codes may not have a matching ZCTA because too few addresses were associated with the specific ZIP Code or the ZIP Code was not the most frequently occurring ZIP Code within any of the blocks where it exists. The ZCTA boundaries in this release are those delineated following the 2020 Census.

NZ Suburbs and Localities describes the spatial extent and name of communities in urban areas (suburbs) and rural areas (localities) for navigation and location purposes.

The suburb and locality boundaries cover New Zealand including North Island, South Island, Stewart Island/Rakiura, Chatham Islands, and nearby offshore islands.

Each suburb and locality is assigned a name, major name, Territorial Authority and, if appropriate, additional in use names. A population estimate is provided for each suburb and locality by Stats NZ.

For more information please refer to the NZ Suburbs and Localities Data Dictionary and the LINZ Website

Changes to NZ Suburbs and Localities can be requested by emailing addresses@linz.govt.nz

Change Request Guidance Documents: - Change Request Process - Change Request Principles, Requirements and Rules

APIs and web services

This dataset is available via ArcGIS Online and ArcGIS REST services, as well as our standard APIs. LDS APIs and OGC web services ArcGIS Online map services

This world cities layer presents the locations of many cities of the world, both major cities and many provincial capitals.Population estimates are provided for those cities listed in open source data from the United Nations and US Census.

Abstract

SDES in Kabul was launched in June 2013, jointly by the Central Statistics Organization (CSO) and the United Nations Population Fund (UNFPA) where the latter provided the technical assistance to the entire survey operations. SDES data serve as the benchmark for demographic information at the district level and to some extent, group of villages/enumeration areas. It is the only survey that addresses the need of local development planners for information at the lower level of disaggregation. There are other surveys that CSO has conducted but these are available only at the national and provincial levels.

To achieve a responsive and appropriate policymaking, statistics plays a vital role. In Afghanistan, there has been a longstanding lack of reliable information at the provincial and district levels which hinders the policy making bodies and development planners to come up with comprehensive plans on how to improve the lives of Afghans. With SDES data, though it is not complete yet for the whole country, most of the important indicators in monitoring the progress towards the achievement of Afghanistan's Millennium Development Goals (MDGs) are being collected.

The main objectives of the survey were: · Gathering data for evidence based decision making, policy, planning and management · Providing data for business and industries · Providing policy and planning for residence housing · Providing data about vulnerable populations · Providing data for the basis of humanitarian assistance · Availability of data for research and analysis

Geographic coverage

Kabul Province Kabul Districts Kabul Villages

Analysis unit

Individuals, households

Universe

The survey covered all de jure household members (usual residents)

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey consisted of two related activities: a) the extensive listing and mapping of houses, establishments and institutions (conducted before the household survey) and b) the household survey.

The listing and mapping covered all houses, businesses and institutions in every village and urban area in Kabul Province and included the preparation of sketch maps on which the physical location of each building structure was marked during the canvassing. The locations of important public services, establishments and institutions such as schools, hospitals, banks, etc., were pinpointed using global positioning system (GPS) devices at a later date.

The surveyors used the mapping outputs to guide them in conducting the survey and ensure complete coverage. In total, 16 nahias, and around 843 villages in 14 districts in Kabul Province were canvassed, divided into 3,068 enumeration areas.

The survey first involved a listing of every household in each village. Half of these listed households (i.e. every other household) were taken as samples and asked questions on education, literacy, employment, migration, functional difficulty, fertility, mortality, parents’ living status, birth registration and household and housing characteristics.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used to collect the survey data. - Listing sheet for village/enumeration area - Household questionnaire - Summary sheets for village/enumeration area

Cleaning operations

Central Statistics Organization (CSO) and UNFPA technical staff were responsible for editing the questionnaires, spot-checking, re-interviewing and recording observations during household interviews in all 16 nahias and 14 districts. This helped to ensure errors were corrected at an early stage of enumeration.

Data encoding and cleaning were also done in Karte-char where 178 encoders were hired and four CSO supervisors were detailed to oversee the whole data processing stage.

The Post Census Group Quarters Review provides communities an opportunity to review the count of residents at group quarters facilities reported in the 2020 Census. In some cases, disruption of Census operations in the early part of the COVID-19 pandemic, access limitations at these facilities or people relocating to other residences around April 1 may have resulted in miscounts. When miscounts are encountered, governmental units can request a review of a facility. Approved corrections are forwarded to the inclusion in the Population Estimates program. More information about the program is available on the Census Bureau website.This map contains data from two sources associated with the 2020 Census.Census block-level population counts from the P.L 94-171 Redistricting Data Set2020 Address Count Listing Files containing counts of group quarter facilities corresponding with the 2020 CensusA group quarter is a place where an organization provides housing as well as meals, custodial, medical or other services. It can include non-institutionalized settings, like college housing and military barracks, as well institutional facilities including correctional centers or nursing homes.This map was created to help Tennessee communities review the number of group quarter facilities in each census block, total group quarter population counts and population counts by group quarter facility type. These include:Correctional facilitiesJuvenile facilitiesNursing facilities/Skilled-nursing facilitiesOther institutional facilitiesCollege/University student housingMilitary quartersOther noninstitutional facilities