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- API opendata.maryland.gov | Last Updated 2017-07-13T22:12:15.000Z
This is a MD iMAP hosted service layer. Find more information at http://imap.maryland.gov. This layer is the MGRS 10 - 000m grid that covers Zone 18S which covers part of MarylandThe U.S. National Grid provides a standardized grid reference system that is seamless across jurisdictional boundaries and allows for pinpointing exact locations. Since USNG is standardized - it can be understood and used as a common geographic framework for response. Zone 18S covers part of Maryland. The vertical UTM boundaries are horizontal latitude band boundaries form (generally) 6 X 8 Grid Zones. Hence - the first three letters of the MGRS value - e.g. '18S' - are referred to as the Grid Zone Designator (GZD). The fourth and fifth characters are a pair of letters identifying one of the 100 - 000-meter grid squares within the grid zone (or UPS area). The S"" in this instance is not to be confused with UTM Zone 18S for UTM in the Southern Hemisphere. There are no classification restrictions on this information. This information was created by the National Geospatial-Intelligence Agency (NGA) Coordinate Systems Analysis Team (SNAC). Last Updated: 01/2016 Feature Service Layer Link: http://geodata.md.gov/imap/rest/services/Location/MD_USNGZone18S/FeatureServer/2 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively the ""Data"") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.
- API data.everettwa.gov | Last Updated 2019-05-29T20:37:49.000Z
Number of library materials lent to the number of persons the library serves. It is the annual circulation divided by the library's legal service area population, and indicates the average number of loans made to each resident annually.
- API performance.seattle.gov | Last Updated 2019-09-12T18:29:20.000Z
This tile measures new City Regulated Affordable Housing. These are affordable homes regulated as rent and income restricted, through regulatory agreements with the City of Seattle Office of Housing including: City Funding from Office of Housing, Multifamily Tax Exemption program (MFTE), Incentive Zoning (IZ), and Mandatory Housing Affordability (MHA). IZ and MHA create affordable units in market rate buildings.
- API performance.seattle.gov | Last Updated 2019-09-12T16:09:36.000Z
EMS incident response
- API kcstat.kcmo.org | Last Updated 2017-04-17T13:53:49.000Z
- API data.bayareametro.gov | Last Updated 2018-07-06T18:06:30.000Z
VITAL SIGNS INDICATOR List Rents (EC9) FULL MEASURE NAME List Rents LAST UPDATED October 2016 DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region. DATA SOURCE real Answers (1994 – 2015) no link Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/ CONTACT INFORMATION firstname.lastname@example.org METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section. Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries. Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville. Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.
- API data.bayareametro.gov | Last Updated 2018-07-06T18:06:55.000Z
VITAL SIGNS INDICATOR Population (LU1) FULL MEASURE NAME Population estimates LAST UPDATED September 2016 DESCRIPTION Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region. DATA SOURCES U.S. Census Bureau 1960-1990 Decennial Census http://factfinder2.census.gov California Department of Finance 1961-2016 Population and Housing Estimates http://www.dof.ca.gov/research/demographic/ CONTACT INFORMATION email@example.com METHODOLOGY NOTES (across all datasets for this indicator) All legal boundaries and names for Census geography (metropolitan statistical area, county, city, tract) are as of January 1, 2010, released beginning November 30, 2010 by the U.S. Census Bureau. A priority development area (PDA) is a locally-designated infill area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are as current as July 2016. Population estimates for PDAs were derived from Census population counts at the block group level for 2000-2014 and at the tract level for 1970-1990. Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970 -2010) and the American Community Survey (2008-2012 5-year rolling average; 2010-2014 5-year rolling average). Population estimates for Bay Area PDAs are from the decennial Census (1970 - 2010) and the American Community Survey (2006-2010 5 year rolling average; 2010-2014 5-year rolling average. Estimates of density for tracts and PDAs use gross acres as the denominator. Annual population estimates for metropolitan areas outside the Bay Area are from the Census and are benchmarked to each decennial Census. The annual estimates in the 1990s were not updated to match the 2000 benchmark. The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside InlandCoastalDelta: American Canyon, Benicia, Clayton, Concord, Cotati, Danville, Dublin, Lafayette, Martinez, Moraga, Napa, Novato, Orinda, Petaluma, Pleasant Hill, Pleasanton, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Walnut Creek, Antioch, Brentwood, Calistoga, Cloverdale, Dixon, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Livermore, Morgan Hill, Oakley, Pittsburg, Rio Vista, Sonoma, St. Helena, Suisun City, Vacaville, Windsor, Yountville Unincorporated: all unincorporated towns
Feed the Future Malawi Interim Survey in the Zone of Infuence, Women's Empowerment in Agriculture Index-Time Use Filedata.usaid.gov | Last Updated 2018-11-12T00:04:19.000Z
This dataset is the second of two datasets needed to calculate the Women’s Empowerment in Agriculture Index (WEAI). It is part of the 2015 Feed the Future Malawi Interim Survey in the Zone of Influence. The survey was designed to monitor program performance by periodic assessments of a number of standardized indicators. A total of 1,021 households were interviewed, which provided data for the target sample size of 1,007 households and ensured the sample is representative of the seven districts covered in the interim assessment. It includes all of the 24-hour time allocation data from Module G6, the time use questionnaire, and thus each respondent in Module G has multiple records—one for each of the 18 time use activities (17,064 records ÷ 18 activities = 948 WEAI respondents). The unique identifiers are pbs_id + idcode + activity.
Public School Enrollments by County, Grade and Gender, Full-Time Out-of-District Special Education 2016-2017 Educationdata.pa.gov | Last Updated 2017-09-28T18:10:38.000Z
2016-2017 enrollments for all publicly funded schools in Pennsylvania as reported by school districts, area vocational-technical schools, charter schools, intermediate units, and state operated educational facilities. Local education agencies were asked to report those students who were enrolled and attending as of October 3, 2016. County and Statewide Totals Notes: Statewide and county totals include counts of students attending education classes on a full-time basis outside their parents' district of residence. This data was obtained from the Bureau of Special Education (PENNDATA 2016). Intermediate Unit and CTC Part-day enrollments are excluded from county and state totals. Statewide and county totals are unique counts of students being educated by public Local Education Agencies. LEA and School level reports may not sum to the County and Statewide totals. Source: Pennsylvania Information Management System (PIMS)
- API internal.open.piercecountywa.gov | Last Updated 2019-07-08T21:38:04.000Z
What is the Point-In-Time Count? The U.S. Department of Housing and Urban Development (HUD) and Washington State Department of Commerce require communities to conduct a one-day Point-In-Time (PIT) Count to survey individuals experiencing homelessness. PIT Counts are one source of data among many that help us understand the magnitude and characteristics of people who are homeless in our community. The Point-In-Time (PIT) Count is a one-day snapshot that captures the characteristics and situations of people living here without a home. The PIT Count includes both sheltered individuals (temporarily living in emergency shelters or transitional housing) and unsheltered individuals (those sleeping outside or living in places that are not meant for human habitation). The annual PIT Count happens the last Friday in January, and is carried out by volunteers who interview people and asks where they slept the night before, where their last residence was located, what may have contributed to their loss of housing, and disabilities the individual may have. It also asks how long the individual has been homeless, age and demographics, and whether the person is a veteran and/or a survivor of domestic violence. Like all surveys, the PIT Count has limitations. Results from the Count are influenced by the weather, by availability of overflow shelter beds, by the number of volunteers, and by the level of engagement of the people we are interviewing. Comparisons from year to year should be done with those limitations in mind.