- API
Assessor [Archived 05-11-2022] - Sales
datacatalog.cookcountyil.gov | Last Updated 2022-05-11T15:57:17.000ZReal estate sales data used by the Cook County Assessor to estimate Fair Cash Values for the purpose of property taxation. Deeds that include multiple PIN numbers are excluded from this data. This data is produced by joining sales data from the Illinois Department of Revenue against incomplete sales data from the Cook County Recorder of Deeds, parsing the resulting data to obtain data unique by deed number, and, in some cases, determining arms' length status of sales.
- API
NCHS - Drug Poisoning Mortality by County: United States
healthdata.gov | Last Updated 2023-07-25T17:57:16.000ZThis dataset contains model-based county estimates for drug-poisoning mortality. Deaths are classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug-poisoning deaths are defined as having ICD–10 underlying cause-of-death codes X40–X44 (unintentional), X60–X64 (suicide), X85 (homicide), or Y10–Y14 (undetermined intent). Estimates are based on the National Vital Statistics System multiple cause-of-death mortality files (1). Age-adjusted death rates (deaths per 100,000 U.S. standard population for 2000) are calculated using the direct method. Populations used for computing death rates for 2011–2016 are postcensal estimates based on the 2010 U.S. census. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years before 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Death rates for some states and years may be low due to a high number of unresolved pending cases or misclassification of ICD–10 codes for unintentional poisoning as R99, “Other ill-defined and unspecified causes of mortality” (2). For example, this issue is known to affect New Jersey in 2009 and West Virginia in 2005 and 2009 but also may affect other years and other states. Drug poisoning death rates may be underestimated in those instances. Smoothed county age-adjusted death rates (deaths per 100,000 population) were obtained according to methods described elsewhere (3–5). Briefly, two-stage hierarchical models were used to generate empirical Bayes estimates of county age-adjusted death rates due to drug poisoning for each year. These annual county-level estimates “borrow strength” across counties to generate stable estimates of death rates where data are sparse due to small population size (3,5). Estimates for 1999-2015 have been updated, and may differ slightly from previously published estimates. Differences are expected to be minimal, and may result from different county boundaries used in this release (see below) and from the inclusion of an additional year of data. Previously published estimates can be found here for comparison.(6) Estimates are unavailable for Broomfield County, Colorado, and Denali County, Alaska, before 2003 (7,8). Additionally, Clifton Forge County, Virginia only appears on the mortality files prior to 2003, while Bedford City, Virginia was added to Bedford County in 2015 and no longer appears in the mortality file in 2015. These counties were therefore merged with adjacent counties where necessary to create a consistent set of geographic units across the time period. County boundaries are largely consistent with the vintage 2005-2007 bridged-race population file geographies, with the modifications noted previously (7,8). REFERENCES 1. National Center for Health Statistics. National Vital Statistics System: Mortality data. Available from: http://www.cdc.gov/nchs/deaths.htm. 2. CDC. CDC Wonder: Underlying cause of death 1999–2016. Available from: http://wonder.cdc.gov/wonder/help/ucd.html. 3. Rossen LM, Khan D, Warner M. Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999–2009. Am J Prev Med 45(6):e19–25. 2013. 4. Rossen LM, Khan D, Warner M. Hot spots in mortality from drug poisoning in the United States, 2007–2009. Health Place 26:14–20. 2014. 5. Rossen LM, Khan D, Hamilton B, Warner M. Spatiotemporal variation in selected health outcomes from the National Vital Statistics System. Presented at: 2015 National Conference on Health Statistics, August 25, 2015, Bethesda, MD. Available from: http://www.cdc.gov/nchs/ppt/nchs2015/Rossen_Tuesday_WhiteOak_BB3.pdf. 6. Rossen LM, Bastian B, Warner M, and Khan D. NCHS – Drug Poisoning Mortality by County: United States, 1999-2015. Available from: https://data.cdc.gov/NCHS/NCHS-Drug-Poisoning-Mortality-by-County-United-Sta/pbkm-d27e. 7. National Center for Health Statistics. County geog
- 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
ED Total Private Covered Employment
internal.open.piercecountywa.gov | Last Updated 2024-04-17T18:09:50.000ZTotal private-sector employment for jobs covered by Unemployment Insurance as reported by employers through the Quarterly Census of Employment and Wages (QCEW).
- 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
Procedure Execution and Projection System
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:15:38.000ZThere is a persistent pressure upon NASA crew members to achieve very high productivity during their missions. Significant challenges exist to maintaining manageable workload while the crew is performing their many and varied tasks allotted for each day while ensuring the crew maintain situation awareness. NASA crew members deal with a large amount of very high technology equipment and perform experiments and procedures that can be extremely long and complex. The solution will require the development of automated management technologies that will operate synergistically with the crew, automating tasks of varying complexity in a dynamic, flexible manner with representations of automation state that the crew is familiar and comfortable with. In this proposal, Cybernet proposes to leverage crew members' capabilities with the design of a distributed Procedure Execution and Projection (PEP) system that focuses on supporting automation of complex procedures while ensuring crew situational awareness and anticipating future problems. Our team will leverage the recent work on the Procedure Representation Language (PRL) and the flexible, distributed and hierarchical capabilities of holonic systems. PRL is an XML encoding of the vehicle/habitat procedures in a form that both crew and automation can use, and the PEP systems' intelligent holonic modules will support crew with a range of capabilities, including automation of procedures, projection of procedures to look for problems and determine courses of action to prevent or mitigate the problems, and make sure that the crew maintain situational awareness of the procedural state. The objectives of the Phase I project are to establish critical requirements for NASA vehicle and habitat crew automation and to design and implement a prototype of the PEP system to demonstrate approach viability.
- API
Approximate Cartesian Control for Robotic Tool Usage with Graceful Degradation Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:31:39.000ZMany of NASA's exploration scenarios include important roles for autonomous or partially autonomous robots. It is desirable for them to utilize human tools when possible, rather than needing to build custom tools for each robot. Control of robotic manipulators for tool usage generally requires a very precise Cartesian-space trajectory of the tool tip (e.g., moving a marker along the surface of a whiteboard or rotating a screwdriver about an axis). Well-known techniques exist for manipulator control in Cartesian space, most of which necessitate solving a series of Inverse Kinematics (IK) problems. Closed-form IK solvers work well for 7-degree-of-freedom (DOF) arms with rigid tool attachments, but cannot handle non-rigid tools that slip in the robot's hands. Numerical IK approaches are more generic and can handle non-rigid links to tools, but can be slow to converge. More importantly, if any joints fail or become limited in their range of motion, the robot arm essentially becomes 6-DOF or lower. IK solvers often fail in these lower DOF spaces because the configuration space becomes non-continuous and full of "holes". As a result, a 7-DOF robotic arm in space might be rendered largely useless if a single joint fails or even loses mobility until it can be serviced. TRACLabs proposes to investigate an alternative approach to traditional Cartesian control approaches, which rely on complex IK solvers that go from Cartesian space backwards to joint space. We propose to leverage cheap memory and modern processing speeds to instead perform simple computations that go from joint space forwards to Cartesian space. Such techniques should overcome common changes to a manipulation chain caused by tool slippage or the grasping of a new tool and to overcome uncommon changes to a chain caused by joint failures, reduced joint mobility, changes in joint geometry or range of motion, or added joints.
- API
Net Zero Porter Ranch: Solar PV Permits by Year
data.lacity.org | Last Updated 2023-05-22T09:33:24.000ZThe Department of Building and Safety issues permits for the construction, remodeling, and repair of buildings and structures in the City of Los Angeles. Permits are categorized into building permits, electrical permits, and mechanical permits (which include plumbing, HVAC systems, fire sprinklers, elevators, and pressure vessels). Depending on the complexity of a project, a permit may be issued the same day with Express Permit or e-Permit ("No Plan Check" category), or a permit may require that the plans be reviewed ("Plan Check" category) by a Building and Safety Plan Check personnel.
- 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.