Parking citation locations in the District of Columbia. The Vision Zero data contained in this layer pertain to parking violations issued by the District of Columbia's Metropolitan Police Department (MPD) and partner agencies with the authority. For example, the District Department of Transportation's (DDOT) traffic control officers write parking violations to prevent congestion through enforcement and control at intersections. Parking violation locations are summarized ticket counts based on time of day, week of year, year, and category of violation. Data was originally downloaded from the District Department of Motor Vehicle's eTIMS meter work order management system. Data was exported into DDOT’s SQL server, where the Office of the Chief Technology Officer (OCTO) geocoded citation data to the street segment level. Data was then visualized using the street segment centroid coordinates.

The dataset contains a subset of locations and attributes of incidents reported in the ASAP (Analytical Services Application) crime report database by the District of Columbia Metropolitan Police Department (MPD). Visit crimecards.dc.gov for more information. This data is shared via an automated process where addresses are geocoded to the District's Master Address Repository and assigned to the appropriate street block. Block locations for some crime points could not be automatically assigned resulting in 0,0 for x,y coordinates. These can be interactively assigned using the MAR Geocoder.On February 1 2020, the methodology of geography assignments of crime data was modified to increase accuracy. From January 1 2020 going forward, all crime data will have Ward, ANC, SMD, BID, Neighborhood Cluster, Voting Precinct, Block Group and Census Tract values calculated prior to, rather than after, anonymization to the block level. This change impacts approximately one percent of Ward assignments.

This dataset contains quality assured and DOEE-certified air quality data collected from the District’s five air monitoring network sites. The dataset covers a three-year period and includes hourly concentration data points from the Environmental Protection Agency (EPA)’s criteria pollutants, air toxics, and speciation. It also includes hourly surface meteorology data points.

Metro Stations (regional). The dataset contains points representing locations and attributes of Metro stations, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Platform centroids were identified from visual observation of orthophotography and extracted planimetric data. Centroids were heads-up digitized from the snapbase. All DC GIS data is stored and exported in Maryland State Plane coordinates NAD 83 meters.

The GRIP DC-8 Dropsonde V3 dataset consists of atmospheric pressure, dry-bulb temperature, dew point temperature, relative humidity, wind direction, wind speed, and fall rate measurements taken during 16 research flights during the Genesis and Rapid Intensification Processes (GRIP) campaign from August 17, 2010 to September 22, 2010. The GRIP campaign was conducted to better understand how tropical storms form and how these storms develop into major hurricanes. The DC-8 Airborne Vertical Atmospheric Profiling System (AVAPS) deploys integrated, highly accurate, GPS-located atmospheric profiling dropsondes to measure and record current atmospheric conditions in a vertical column below the aircraft. The dropsondes are ejected from a tube in the underside of the DC-8 aircraft. As the dropsonde descends to the surface via a parachute, it continuously measures and transmits data to the aircraft using a 400 MHz meteorological band telemetry link. Pressure, temperature and relative humidity, as well as GPS-based wind data were collected from 328 dropsondes. These Dropsonde data are in ASCII-csv file format.

Zip Codes (5-digit). The dataset polygons represent location and attributes of zip codes, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Zip Codes were identified from public records (US Postal Service) and created selecting arcs from the street centerlines and vector property map.

Context

This dataset expands on my earlier New York City Census Data dataset. It includes data from the entire country instead of just New York City. The expanded data will allow for much more interesting analyses and will also be much more useful at supporting other data sets.

Content

The data here are taken from the DP03 and DP05 tables of the 2015 American Community Survey 5-year estimates. The full datasets and much more can be found at the American Factfinder website. Currently, I include two data files:

1. acs2015_census_tract_data.csv: Data for each census tract in the US, including DC and Puerto Rico.
2. acs2015_county_data.csv: Data for each county or county equivalent in the US, including DC and Puerto Rico.

The two files have the same structure, with just a small difference in the name of the id column. Counties are political subdivisions, and the boundaries of some have been set for centuries. Census tracts, however, are defined by the census bureau and will have a much more consistent size. A typical census tract has around 5000 or so residents.

The Census Bureau updates the estimates approximately every year. At least some of the 2016 data is already available, so I will likely update this in the near future.

Acknowledgements

The data here were collected by the US Census Bureau. As a product of the US federal government, this is not subject to copyright within the US.

Inspiration

There are many questions that we could try to answer with the data here. Can we predict things such as the state (classification) or household income (regression)? What kinds of clusters can we find in the data? What other datasets can be improved by the addition of census data?

This dataset contains model-based ZIP Code Tabulation Area (ZCTA) level estimates for the PLACES 2022 release in GIS-friendly format. PLACES covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code Tabulation Area levels. It provides information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. PLACES was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2020 or 2019 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2015–2019 estimates. The 2022 release uses 2020 BRFSS data for 25 measures and 2019 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening) that the survey collects data on every other year. These data can be joined with the census 2010 ZCTA boundary file in a GIS system to produce maps for 29 measures at the ZCTA level. An ArcGIS Online feature service is also available for users to make maps online or to add data to desktop GIS software. https://cdcarcgis.maps.arcgis.com/home/item.html?id=3b7221d4e47740cab9235b839fa55cd7

The Chief Technology Office (OCTO) has captured locations for many of the District of Columbia's museums. This includes museums operated by government and private organizations. DC's museums and cultural centers are many and therefore this dataset should not be considered a complete list.

This 2021 Population Census dataset contains statistics relevant to demographic, household, educational, economic, housing and internal migration characteristics of the Hong Kong population residing in the 18 District Council districts in 2021. The dataset also contains the boundaries of individual District Council districts. Since 1961, a population census has been conducted in Hong Kong every 10 years and a by-census in the middle of the intercensal period. The 2021 Population Census, which was conducted in June to August 2021, provides benchmark statistics on the socio-economic characteristics of the Hong Kong population vital to the planning and policy formulation of the government. This dataset will be incorporated into Population Distribution Framework Spatial Data Theme.

Counting Results of 2015 District Council Election at Individual Counting Stations in Southern District (CSV)

Population-based county-level estimates for prevalence of DC were obtained from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (16). DC prevalence rate was defined as the propor-tion of people within a county who had previously been diagnosed with diabetes (high fasting plasma glu-cose 126 mg/dL, hemoglobin A1c (HbA1c) of 6.5%, or diabetes diagnosis) but do not currently have high fasting plasma glucose or HbA1c for the period 2004-2012. DC prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or HbA1C levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (16). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or HbA1C status for each BRFSS respondent (16). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict county-level prevalence of diabetes-related outcomes, including DC (16). The EQI was constructed for 2006-2010 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that _domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each _domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and _domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). Results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing the highest quintile/worst environmental quality to the lowest quintile/best environmental quality expo-sure metrics. PRDs are representative of the entire period of interest, 2004-2012. Due to availability of DC data and covariate data, not all counties were captured, however, the majority, 3134 of 3142 were utilized in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, K. Price, D. Lobdell, and R. Sargis. Diabetes control is associated with environmental quality in the USA. Endocrine Connections. BioScientifica Ltd., Bristol, UK, 10(9): 1018-1026, (2021).

Consumption data for coal, petroleum, and natural gas are multiplied by their respective thermal conversion factors, which are in units of heat energy per unit of fuel consumed (i.e., per cubic foot, barrel, or ton), to calculate the amount of heat energy derived from fuel combustion. The thermal conversion factors are given in Appendix A of each issue of Monthly Energy Review, published by the Energy Information Administration (EIA) of the U.S. Department of Energy (DOE). Results are expressed in terms of heat energy obtained from each fuel type. These energy values were obtained from the State Energy Data Report (EIA, 2003a), ( http://www.eia.doe.gov/emeu/states/sep_use/total/csv/use_csv.html), and served as our basic input. The energy data are also available in hard copy from the Energy Information Administration, U.S. Department of Energy, as the State Energy Data Report (EIA, 2003a,b). For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/trends/emis_mon/stateemis/emis_state.html

The dataset contains locations and attributes of Hotels, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. A database provided by the DC Taxi Commission (DCTC) and research at various commercial websites identified Hotels and DC GIS staff geo-processed the data.

Data files in this repository contain a static archive of all the observational and model data required to construct fossil-fuel enhancements at tower sites in Los Angeles (LA_data.csv) and DC-Baltimore (DCBalt_data.csv). These were used in the analyses detailed and presented in the publication "The impact of COVID-19 on CO2 emissions in the Los Angeles and Washington DC/Baltimore metropolitan areas" by Yadav et al.

Sex Offender work and home locations, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. If users want to obtain more information about sex offenders, they should go to the Sex Offender Mapping Application (https://sexoffender.dc.gov/) and download the “More Details” PDF. Data provided by the Court Services and Offender Supervision Agency identified sex offender registry providing location at the block level. https://www.csosa.gov/.

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. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

The increase in power electronic based generation sources require accurate modeling of inverters. Accurate modeling requires experimental data over wider operation range. We used 8.35 kW off-the-shelf grid following split phase PV inverter in the experiments. We used controllable AC supply and controllable DC supply to emulate AC and DC side characteristics. The experiments were performed at NREL's Energy Systems Integration Facility. Inverter is tested under 100%, 75%, 50%, 25% load conditions. In the first dataset, for each operating condition, controllable AC source voltage is varied from 0.9 to 1.1 per unit (p.u) with a step value of 0.025 p.u while keeping the frequency at 60 Hz. In the second dataset, under similar load conditions (100%, 75%, 50%, 25% ), the frequency of the controllable AC source voltage was varied from 59 Hz to 61 Hz with a step value of 0.2 Hz. Voltage and frequency range is chosen based on inverter protection. Voltages and currents on DC and AC side are included in the dataset.

Dati anagrafici delle Aree Organizzative Omogenee di cui agli articoli 50 e 61 del D.P.R. n. 445/2000 corrispondenti ai registri di protocollo dell' ente. Il dataset contiene i domicili digitali di ciascuna Area Organizzativa Omogenea ovvero gli indirizzi elettronici eletti presso un servizio di posta elettronica certificata o un servizio elettronico di recapito certificato qualificato validi ai fini delle comunicazioni elettroniche aventi valore legale [art. 1 comma 1 lettera n-ter) del CAD]. I domicili digitali delle AOO costituiscono l'insieme dei domicili digitali dell'ente a cui appartengono che é individuato dal valore del campo Codice_IPA (il codice può essere utilizzato per reperire ulteriori dettagli nel dataset Amministrazioni). Ciascuna AOO é identificata univocamente in IPA dal Codice_uni_aoo assegnato dal sistema; all'interno del singolo ente l'AOO é identificata dal cod_AOO assegnato dall'ente stesso.

È possibile scaricare il dataset nei seguenti formati; il nome del file prodotto coincide con il resource_id ed è: cdaded04-f84e-4193-a720-47d6d5f422aa

• https://indicepa.gov.it/ipa-dati/datastore/dump/cdaded04-f84e-4193-a720-47d6d5f422aa?bom=True" download="cdaded04-f84e-4193-a720-47d6d5f422aa.csv" > CSV

• https://indicepa.gov.it/ipa-dati/datastore/dump/cdaded04-f84e-4193-a720-47d6d5f422aa?bom=True&format=tsv" download="cdaded04-f84e-4193-a720-47d6d5f422aa.tsv" > TSV

• https://indicepa.gov.it/ipa-dati/datastore/dump/cdaded04-f84e-4193-a720-47d6d5f422aa?bom=True&format=json" download="cdaded04-f84e-4193-a720-47d6d5f422aa.json" > JSON

• https://indicepa.gov.it/ipa-dati/datastore/dump/cdaded04-f84e-4193-a720-47d6d5f422aa?bom=True&format=xml" download="cdaded04-f84e-4193-a720-47d6d5f422aa.xml" > XML

• https://indicepa.gov.it/ipa-dati/dataset/15c8cc52-749a-4f64-9bd1-9c3bb4f6f3df/resource/cdaded04-f84e-4193-a720-47d6d5f422aa/download/aree_organizzative_omogenee.xlsx" download="cdaded04-f84e-4193-a720-47d6d5f422aa.xlsx" > XLSX

The dataset contains lines representing Metro lines in the Washington DC Metropolitan area. Lines were taken from legacy data from WMATA and fit to orthophotography and extracted planimetric data.