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Assessor Parcels Data - 2010
data.lacounty.gov | Last Updated 2021-08-02T18:13:16.000ZValuation and property description for parcels on the Assessor's annual secured assessment roll for 2010. Default sort is by AssessorID. This dataset excludes Cross Reference Roll (89xx-series AssessorID).
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GPM, DPR, GMI Level 3 Combined Precipitation V03
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:03:54.000ZThere are uncertainties in the interpretation of data from any one of the instruments (KuPR, KaPR, and GMI). By using data from multiple instruments, further constraints on the solution of precipitation structure improve the final product.The purpose of 3CMB is to give a daily and monthly accumulation of the 2BCMB precipitation product. The 3CMB product is a daily and monthly accumulation of the 2BCMB orbital combined product at two grid sizes, 5 x 5 degrees (G1) and 0.25 x 0.25 degrees (G2). Grid G1 contains the following physical measurements of general interest, among others. Grid G2 contains the same groups, but it is on the ltH x lnH grid and does not have the surface type (st) dimension or the histograms (see dimension definitions below). Below, conditional products represent means based upon precipitating areas only; unconditional products represent means for raining and non-raining areas combined. Probabilities represent the number of raining observations divided by the total number of raining and non-raining observations. precipTotRate (Group in G1)- Conditional mean rate for all precipitation phases (ice, liquid, mixed-phase). * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqRate (Group in G1) - Conditional mean rate for liquid precipitation. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotWaterContent (Group in G1) - Conditional mean water content for all precipitation phases. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipLiqWaterContent (Group in G1) - Conditional mean liquid water content. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, g/m3. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, g/m3. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotDm (Group in G1) - Conditional mass-weighted mean particle diameter. * count (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st): Count. * mean (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Mean, mm. * stdev (4-byte float, array size: ltL x lnL x ns x hgt x rt x st): Standard deviation for the monthly product. Mean of squares for the daily product, mm. * hist (4-byte integer, array size: ltL x lnL x ns x hgt x rt x st x bin): Histogram. precipTotRateDiurnal (Group in G1) - Conditional mean total surface precipitation rate indexed by local time. * count (4-byte integer, array size: ltL x lnL x ns x st x tim): Count. * mean (4-byte float, array size: ltL x lnL x ns x st x tim): Mean, mm/h. * stdev (4-byte float, array size: ltL x lnL x ns x st x tim): Standard deviation for the monthly product. Mean of squares for the daily product, mm/h. surfPrecipTotRateDiurnalAllObs (4-byte integer, array size: ltL x lnL x ns x st x tim): Number of total observa...
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Bathrooms
data.acgov.org | Last Updated 2020-01-06T13:36:53.000ZAlameda County Restaurants Inspections April 30, 2012 to Current
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Parcels 2015 Map
data.smgov.net | Last Updated 2019-02-15T20:27:55.000Z - API
bathrooms and stuff
data.acgov.org | Last Updated 2020-01-06T13:36:53.000ZAlameda County Restaurants Inspections April 30, 2012 to Current
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TRMM Microwave Imager (TMI) Gridded Oceanic Rainfall Product (TRMM Product 3A11) V7
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T04:52:56.000ZThe Tropical Rainfall Measuring Mission (TRMM) is a joint U.S.-Japan satellite mission to monitor tropical and subtropical precipitation and to estimate its associated latent heating. TRMM was successfully launched on November 27, at 4:27 PM (EST) from the Tanegashima Space Center in Japan. The TRMM Microwave Imager (TMI) is a nine-channel passive microwave radiometer, which builds on the heritage of the Special Sensor Microwave/Imager (SSM/I) instrument flown aboard the Defense Meteorological Satellite Program (DMSP) platforms. Microwave radiation is emitted by the Earth's surface and by water droplets within clouds. However, when layers of large ice particles are present in upper cloud regions - a condition highly correlated with heavy rainfall - microwave radiation tends to scatter at frequencies above 19 GHz. The TMI detects radiation at five frequencies chosen to discriminate among these processes, thus revealing the likelihood of rainfall. The key to accurate retrieval of rainfall rates by this method is the deduction of cloud precipitation consistent with the radiation measurement at each frequency. The TMI frequencies are 10.65, 19.35, 37 and 85.5 GHz (dual polarization), and 21 GHz (vertical polarization only). The TMI Gridded Oceanic Rainfall Product, also known as TMI Emission, consists of 5 degree by 5 degree monthly oceanic rainfall maps using TMI Level 1 data as input. Statistics of the monthly rainfall, including number of samples, standard deviation, goodness-of-fit (of the brightness temperature histogram to the lognormal rainfall distribution function) and rainfall probability are also included in the output for each grid box. Spatial coverage is between 40 degrees North and 40 degrees South owing to the 35 degree inclination of the TRMM satellite. TMI brightness temperature histograms at 1 degree intervals are generated based on the 19, 21 and 19-21 GHz combination channels obtained from the Level 1B (calibrated brightness temperature) TMI product. Monthly rainfall indices over the ocean are derived by statistically matching monthly histograms of brightness temperatures with model calculated rainfall Probability Distribution Functions (PDF) using the 19-21 GHz combination data. Retrieved monthly rainfall data must pass a quality test based on the quality of the PDF fit. The data are stored in the Hierarchical Data Format (HDF), which includes both core and product specific metadata applicable to the TMI measurements. A file contains 12 arrays of rainfall data and supporting information each of dimension 72 x 16, with a file size of about 40 KB (uncompressed). The HDF-EOS "grid" structure is used to accommodate the actual geophysical data arrays. There is 1 file of TMI 3A11 data produced per month.
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Building information 2017 map
data.melbourne.vic.gov.au | Last Updated 2022-12-08T21:12:38.000ZData collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). It shows selected building attributes including location, construction year, refurbished year, number of floors above ground, predominant space use, bicycle/shower facilities and building accessibility. Building accessibility data is collected to track accessibility for internal City of Melbourne purposes. This data is provided as a community service by the City of Melbourne. It is not and does not purport to be a complete guide. There may be errors or omissions. Data is liable to change. The City of Melbourne accepts no responsibility in respect of any claim arising from use or reliance upon this data. For more information about CLUE see http://www.melbourne.vic.gov.au/clue
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GPM PR and TMI on TRMM Combined Convective-Stratiform Latent Heating Profiles L2 1.5 hours 5 km V06 (GPM_2HCSH_TRMM) at GES DISC
data.nasa.gov | Last Updated 2022-01-17T05:25:46.000ZThis is a new (GPM-formated) TRMM product. The equivalent old TRMM legacy product is TRMM_2H31. Version 06 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 06. Estimating vertical profiles of latent heating released by precipitating cloud systems is one of the key objectives of TRMM, together with accurately measuring the horizontal distribution of tropical rainfall. The method uses TRMM PR information [precipitation-top height (PTH), precipitation rates at the surface and melting level, and rain type] to select heating profiles from lookup tables. Heating-profile lookup tables for the three rain types—convective, shallow stratiform, and anvil rain (deep stratiform with a melting level)—were derived from numerical simulations of tropical cloud systems from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) utilizing a cloud-resolving model (CRM). The CSH algorithm is severely limited by the inherent sensitivity of the TRMM PR. For latent heating, the quantity required is actually cloud top, but the PR can detect only precipitation-sized particles. Because observed information on precipitation depth is used in addition to precipitation type and intensity, differences between shallow and deep convection are more distinct in the CSH algorithm in comparison with the CSH algorithm.
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BHO MH Engagement in Care: 2010-2014
data.ny.gov | Last Updated 2019-06-10T18:02:12.000ZThe Behavioral Health Organization (BHO) initiative oversees the transition to managed care for Medicaid recipients who receive mental health (MH) and substance use disorder (SUD) services in New York State. The metrics emphasize improving rates of timely follow-up treatment post discharge, timely filling of appropriate medication prescriptions post discharge, and reducing rates of readmission.The BHO MH Engagement in Care dataset is designed to assess the degree to which individuals discharged from mental health inpatient treatment engage in outpatient treatment post discharge where "engagement" is defined as receiving two or more outpatient mental health visits within thirty days of discharge and the degree to which individuals discharged from mental health inpatient treatment engage in outpatient treatment post discharge where "engagement" is defined as receiving four or more outpatient mental health visits within 60 days of discharge. The year 2015 saw the conclusion of the first phase of the Behavioral Health Organization initiative (BHO). A new Behavioral Health Managed Care Transition phase II is underway. The data contained in the BHO metrics span 2010 to 2014, using the 2010 calendar year for a baseline. Earlier in the program (2011‐2012) the metrics were calculated quarterly and on a year‐to‐date basis, later in (2013‐2014), New York State Office of Mental Health opted for semi‐annual and year‐to‐date aggregations.
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HCD Property Investments FY10-Q1 22
data.memphistn.gov | Last Updated 2022-05-18T19:48:11.000ZAddress-level dataset of of properties receiving HCD services from July 1, 2009 - March 31, 2022 (FY10-Q1 22). Data was combined in June-July 2020 from multiple sources, including IDIS (HUD data and reimbursement system), tracking spreadsheets maintained by HCD managers, and WAPez (weatherization data system).