- API
Bureau of Street Lighting (BSL) - Cumulative # of street lights converted to LED (Monthly)
data.lacity.org | Last Updated 2020-11-30T17:02:37.000ZThis dataset contains metrics that measure the operational performance of the Bureau of Street Lighting. These metrics are used on a regular basis by the department and the Mayor to evaluate progress and inform decision making. Performance management forms the foundation of a data-driven culture of innovation and excellence in the City of Los Angeles.
- API
Neighborly ERA Applications
sharefulton.fultoncountyga.gov | Last Updated 2024-02-20T02:51:22.000ZThis dataset contains all applicants for emergency rental and/or utility assistance in the Neighborly system.
- API
ICI - Retail Garbage Composition - Material Category
data.calgary.ca | Last Updated 2023-02-01T15:39:38.000ZThis chart shows weight per cent composition grouped by material category. This dataset is for garbage bin waste from the Industrial, Commercial, and Institutional (ICI) sector. The North American Industry Classification System (NAICS) is used to categorize ICI businesses and organizations into sub-sectors for ease of data collection and reporting. The NAICS sub-sectors included in this study are: Accommodation and Food Services (NAICS code 72), Retail Trade (44-45), Manufacturing (31-33), Health Care and Social Assistance (62), and Public Administration (91). All businesses and organizations included in the study were customers of The City’s Commercial Collections service, except for one privately-serviced customer in the Accommodation and Food Services sub-sector that was added to provide better sector representation. A total of 115 samples are included in the dataset: 30 in Accommodation and Food Services, 35 in Retail Trade, 17 in Manufacturing, 25 in Health Care and Social Assistance, and 8 in Public Administration. The weight per cent for each sub-sector is the pooled average of samples collected in the four seasons of 2019. Waste Composition studies are periodically conducted by Waste and Recycling Services to help assess the performance of diversion and education programs and inform improvements and new program design.
- API
Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, 1996
nasa-test-0.demo.socrata.com | Last Updated 2015-07-19T07:26:48.000ZThe Agency for Toxic Substances and Disease Registry (ATSDR) Hazardous Waste Site Polygon Data, 1996 consists of 2042 polygons for selected hazardous waste sites that were compiled in January 1996. The Hazardous Waste Site ATSDR layer was created by linking HAZ_SITES_ATSDR_BASE with additional data. Most polygons represent sites considered for cleanup under the Comprehensive Environmental Response, Compensation and Liability Act (CERCLA or Superfund). Typical sites are either on the EPA National Priorities List (NPL) or are being considered for inclusion on the NPL. This dataset is distributed by the Columbia University Center for International Earth Science Information Network (CIESIN). (Suggested Usage: To provide a polygon dataset of hazardous waste sites in the United States, which can be used to identify nearby populations and assess their potential risk)
- API
TRMM (TMPA-RT) Near Real-Time Precipitation L3 1 day 0.25 degree x 0.25 degree V7 (TRMM_3B42RT_Daily) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:59:46.000ZTMPA (3B42RT_Daily) dataset have been discontinued as of Dec. 31, 2019, and users are strongly encouraged to shift to the successor IMERG dataset (doi: 10.5067/GPM/IMERGDE/DAY/06; 10.5067/GPM/IMERGDL/DAY/06). This daily accumulated precipitation product is generated from the Near Real-Time 3-hourly TRMM Multi-Satellite Precipitation Analysis TMPA (3B42RT). It is produced at the NASA GES DISC, as a value added product. Simple summation of valid retrievals in a grid cell is applied for the data day. The result is given in (mm). Although the grid is from 60S to 60N, the high latitudes (beyond 50S/N) near real-time retrievals are considered very unreliable and thus are screened out from the daily accumulations. The beginning and ending time for every daily granule are listed in the file global attributes, and are taken correspondingly from the first and the last 3-hourly granules participating in the aggregation. Thus the time period covered by one daily granule amounts to 24 hours, which can be inspected in the file global attributes. Counts of valid retrievals for the day are provided for every variable, making it possible to compute conditional and unconditional mean precipitation for grid cells where less than 8 retrievals for the day are available. Efforts have been made to make the format of this derived product as similar as possible to the new Global Precipitation Measurement CF-compliant file format. The latency of this derived daily product is about 7 hours after the UTC day is closed. Users should be mindful that the price for the short latency of these data is the reduced quality as compared to the research quality product. The information provided here on the TRMM mission, and on the original 3-hr 3B42 product, remain relevant for this derived product. Note, however, this product is in netCDF-4 format. The following describes the derivation in more details. The daily accumulation is derived by summing *valid* retrievals in a grid cell for the data day. Since the 3-hourly source data are in mm/hr, a factor of 3 is applied to the sum. Thus, for every grid cell we have Pdaily = 3 * SUM{Pi * 1[Pi valid]}, i=[1,Nf] Pdaily_cnt = SUM{1[Pi valid]} where: Pdaily - Daily accumulation (mm) Pi - 3-hourly input, in (mm/hr) Nf - Number of 3-hourly files per day, Nf=8 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day. Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that Pi=0 is a valid value. On occasion, the 3-hourly source data have fill values for Pi in a very few grid cells. The total accumulation for such grid cells is still issued, inspite of the likelihood that thus resulting accumulation has a larger uncertainty in representing the "true" daily total. These events are easily detectable using "counts" variables that contain Pdaily_cnt, whereby users can screen out any grid cells for which Pdaily_cnt less than Nf. There are various ways the accumulated daily error could be estimated from the source 3-hourly error. In this release, the daily error provided in the data files is calculated as follows. First, squared 3-hourly errors are summed, and then square root of the sum is taken. Similarly to the precipitation, a factor of 3 is finally applied: Perr_daily = 3 * { SUM[ (Perr_i * 1[Perr_i valid])^2 ] }^0.5 , i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] ) where: Perr_daily - Magnitude of the daily accumulated error power, (mm) Ncnt_err - The counts for the error variable Thus computed Perr_daily represents the worst case scenario that assumes the error in the 3-hourly source data, which is given in mm/hr, is accumulating within the 3-hourly period of the source data and then during the day. These values, however, can easily be conveted to root mean square error estimate of the rainfall rate: rms_err = { (Perr_daily/3) ^2 / Ncnt
- API
EMS INcidents Chloropleth Map Fall Injury Counts by Zip Code Current Calendar Year
data.marincounty.org | Last Updated 2024-04-05T13:30:40.000ZEmergency Medical Service ambulance dispatch incidents in Marin County, CA, for the period beginning March, 2013 through September, 2019. Data is updated every three to six months. Data includes time stamps of events for each dispatch, nature of injury, and location of injury. Data also includes geocoding of most incident locations, however, specific street address locations are "obfuscated" and are generally shown within a block and are not, therefore, exact locations. Geocoding results are also based on the quality of the address information provided, and should therefore not be considered 100% accurate. Some of the data may be interpreted incorrectly without adequate knowledge of the clinical context. Please contact EMS@marincounty.org if you have any questions about the interpretation of fields in this dataset.
- API
India Annual Winter Cropped Area, 2001-2016
data.nasa.gov | Last Updated 2022-01-17T05:29:43.000ZThe India Annual Winter Cropped Area, 2001 - 2016 consists of annual winter cropped areas for most of India (except the Northeastern states) from 2000-2001 to 2015-2016. This data set utilizes the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI; spatial resolution: 250m) for the winter growing season (October-March). The methodology uses an automated algorithm identifying the EVI peak in each pixel for each year and linearly scales the EVI value between 0% and 100% cropped area within that particular pixel. Maps were then resampled to 1 km and were validated using high-resolution QuickBird, RapidEye, SkySat, and WorldView-2 images spanning 2008 to 2016 across 11 different agricultural regions of India. The spatial resolution of the data set is 1 km, resampled from 250m. The data are distributed as GeoTIFF and NetCDF files and are in WGS 84 projection.
- API
Dallas Police Public Data - OIS 2005
www.dallasopendata.com | Last Updated 2023-03-07T20:57:55.000ZDallas Police Public Data - Officer Involved Shootings City Of Dallas
- API
ICI - Retail Garbage Composition - Proper Disposal Location
data.calgary.ca | Last Updated 2023-02-01T15:39:39.000ZThis chart shows weight per cent composition grouped by proper disposal location. This dataset is for garbage bin waste from the Industrial, Commercial, and Institutional (ICI) sector. The North American Industry Classification System (NAICS) is used to categorize ICI businesses and organizations into sub-sectors for ease of data collection and reporting. The NAICS sub-sectors included in this study are: Accommodation and Food Services (NAICS code 72), Retail Trade (44-45), Manufacturing (31-33), Health Care and Social Assistance (62), and Public Administration (91). All businesses and organizations included in the study were customers of The City’s Commercial Collections service, except for one privately-serviced customer in the Accommodation and Food Services sub-sector that was added to provide better sector representation. A total of 115 samples are included in the dataset: 30 in Accommodation and Food Services, 35 in Retail Trade, 17 in Manufacturing, 25 in Health Care and Social Assistance, and 8 in Public Administration. The weight per cent for each sub-sector is the pooled average of samples collected in the four seasons of 2019. Waste Composition studies are periodically conducted by Waste and Recycling Services to help assess the performance of diversion and education programs and inform improvements and new program design.
- API
Ultra Low Noise 1.06 Micron Laser Oscillator Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:29:00.000ZThe Laser Interferometer Space Antenna (LISA) demand state-of-the-art ultra-stable and low noise coherent lasers. This is a proposal to develop a space qualified high power, single mode, low noise and narrow linewidth fiber laser based on a"virtual ring" laser cavity at the 1.06 micron spectral band. This novel laser architecture enables traveling-wave oscillation in a compact, linear and all-fiber cavity. This leads to unprecedented low noise and stable laser oscillator. The all fiber device also offers a highly reliable, compact and power conserving solution. We have already demonstrated virtual ring oscillators at the 1.55 micron band that rival the state of the ring laser architecture. In this research we will develop a 1.06 micron laser that can meet or exceed the LISA experiment required laser specifications.