City staff and community partners work together to survey people experiencing homelessness in Ottawa. So far, the City has led two counts:April 2018October 2021Oct 2024The survey is conducted to gather information about people experiencing homelessness. The goal of this work is to guide new approaches to address homelessness at a local level and help in the planning and delivery of services.Date created: 28 April 2022Update frequency: As needed.Accuracy: Convenience sampling was used to recruit survey respondents. This method of recruiting respondents to answer the survey does not rely on a random selection process. Instead, surveyors approach potential respondents if they are close by at the time the surveyor is delivering the questionnaire. Many factors could determine participation in the survey including:Number of community partners involved in the PiT countLocation of surveyors and their physical proximity to potential respondentsNumber of engagement eventsSeason the survey was conductedDifferences in results between PiT count years may be due to changes within the homeless population and shifts in methodology. For comparisons of emergency shelter use over time, visit the Temporary Emergency Accommodations Dashboard. An analysis of factors related to housing and homelessness during COVID-19 provides context for unique housing market conditions during the pandemic.Results shown in the Survey results: Point-in-Time count dashboard are presented by sector. The name and definition of each sector are below:All: All respondents who answered the surveySingle adult: Respondents aged 25 years or older and not accompanied by anyoneUnaccompanied youth: Respondents under 25 years old and not accompanied by anyoneFamily: Respondents accompanied by children under 18 years oldAttributes:Question: The question that was asked in the surveyTopic: The classification of the survey question by themSector: Refers to the population (total, family, unaccompanied youth, single adults)Period: Month the Point-in-Time count was conductedResponse: Response category of the survey questionNumeratorDenominatorPercentage Author: Housing ServicesAuthor email: pitcount_denombrementponctuel@ottawa.ca

Due to the unprecedented COVID-19 pandemic, the US Department of Housing and Urban Development (HUD) approved the Maricopa Regional Continuum of Care to opt out of the unsheltered Point In Time (PIT) Homeless Count for 2021. Every January, volunteers and outreach teams from local communities collaborate to survey and count the number of homeless. persons in their respective locations. With the information provided by the PIT Count, the Maricopa Regional Continuum of Care and local communities can determine how best to address homelessness. For more information see https://www.azmag.gov/Programs/Homelessness/Point-In-Time-Homeless-Count">https://www.azmag.gov/Programs/Homelessness/Point-In-Time-Homeless-Count.

NOTE: The HUD definition of chronic homelessness is: (1) a person who lives in a place not meant for human habitation, Safe Haven, or Emergency Shelter, (2) has a disability, and (3) has been homeless continuously for one year OR four or more times homeless in the last three years, where the combined length of time homeless is at least 12 months.

**Mesa 2025 Data: 6 interactions documented in cities outside of Mesa. Geolocation confirmed interactions occurred in Mesa, versus documented city. Dataset manually updated to reflect correct interaction location and correct PIT counts reflected in https://maricopacoc.org/data/point-in-time-count/">Maricopa Regional Continuum of Care .

The service delivers information about lightning discharges in Sweden. Discharge data has been calculated by a lightning information computational server based on observations made by automatic lightning localisation sensors. The Lightning locating system records separate discharges (en.g. “stroke”). What we humans perceive as a lightning. “flash”) may consist of multiple discharges. Discharges with “chi square” >= 10.0 have been excluded from the data set for quality reasons. Note that these data contain some uncertainty and should therefore be used with caution for statistical processing. Data are not homogeneous over time as the lightning localisation system is constantly changing and improving. For example, sensors can be upgraded or system configuration changed. In 2014, SMHI changed the calculation server in the lightning localisation system. Archived discharge data are based on calculations with the previous calculation server (LP2000) up to and including 2014-03-25, then data is based on the current computational server (TLP). This means that, from this point in time, data are not directly comparable with older data. The information delivered is based on the UALF format (Universal ASCII Lightning Format). The 25 fields delivered for each discharge are described in tabular form in the API documentation here: https://opendata.smhi.se/apidocs/lightning/parameters.html

This report displays the data communities reported to HUD about the nature of and amount of persons who are homeless as part of HUD's Point-in-Time (PIT) Count. This data is self-reported by communities to HUD as part of its competitive Continuum of Care application process. The website allows users to select PIT data from 2005 to present. Users can use filter by CoC, states, or the entire nation.

This indicator presents available data at national level on the number of people reported by public authorities as homeless. Data are drawn from the OECD Questionnaire on Affordable and Social Housing (QuASH 2021, QuASH 2019, QuASH 2016) and other available sources. Overall, homelessness data are available for 36 countries (Table HC 3.1.1 in Annex I). Further discussion of homelessness can be found in the 2020 OECD Policy Brief, “Better data and policies to fight homelessness in the OECD”, available online (and in French). Discussion of national strategies to combat homelessness can be found in indicator HC3.2 National Strategies for combating homelessness. Comparing homeless estimates across countries is difficult, as countries do not define or count the homeless population in the same way. There is no internationally agreed definition of homelessness. Therefore, this indicator presents a collection of available statistics on homelessness in OECD, EU and key partner countries in line with definitions used in national surveys (comparability issues on the data are discussed below). Even within countries, different definitions of homelessness may co-exist. In this indicator, we refer only to the statistical definition used for data collection purposes. Detail on who is included in the number of homeless in each country, i.e. the definition used for statistical purposes, is presented in Table HC 3.1.2 at the end of this indicator. To facilitate comparison of the content of homeless statistics across countries, it is also indicated whether the definition includes the categories outlined in Box HC3.1, based on “ETHOS Light” (FEANTSA, 2018). Homelessness data from 2020, which are available for a handful of countries and cover at least part of the COVID-19 pandemic, add an additional layer of complexity to cross-country comparison. The homeless population estimate in this case depends heavily on the point in time at which the count took place in the year, the method to estimate the homeless (through a point-in-time count or administrative data, as discussed below), the existence, extent and duration of emergency supports introduced in different countries to provide shelter to the homeless and/or to prevent vulnerable households from becoming homeless (such as eviction bans). Where they are available, homeless data for 2020 are thus compared to data from the previous year in order to facilitate comparison with other countries.

Towns in Time is a compilation of time series data for Victoria's towns
covering the years 1981 to 2011. The data is based on Census data collected by
the Australian Bureau of Statistics. Towns in Time presents 2011 data for the
2011 definition of each town, together with data under the 2006 definition for
2006 and earlier years. A map showing the difference in the town's boundaries
between 2006 and 2011 is attached to each data sheet. It is recommended the
user assess this concordance when using time series data.

Towns in Time is a compilation of time series data for Victoria's towns covering the years 1981 to 2011. The data is based on Census data collected by the Australian Bureau of Statistics. Towns in Time presents 2011 data for the 2011 definition of each town, together with data under the 2006 definition for 2006 and earlier years. A map showing the difference in the town's boundaries between 2006 and 2011 is attached to each data sheet. It is recommended the user assess this concordance when using time series data.

analyze the current population survey (cps) annual social and economic supplement (asec) with r the annual march cps-asec has been supplying the statistics for the census bureau's report on income, poverty, and health insurance coverage since 1948. wow. the us census bureau and the bureau of labor statistics ( bls) tag-team on this one. until the american community survey (acs) hit the scene in the early aughts (2000s), the current population survey had the largest sample size of all the annual general demographic data sets outside of the decennial census - about two hundred thousand respondents. this provides enough sample to conduct state- and a few large metro area-level analyses. your sample size will vanish if you start investigating subgroups b y state - consider pooling multiple years. county-level is a no-no. despite the american community survey's larger size, the cps-asec contains many more variables related to employment, sources of income, and insurance - and can be trended back to harry truman's presidency. aside from questions specifically asked about an annual experience (like income), many of the questions in this march data set should be t reated as point-in-time statistics. cps-asec generalizes to the united states non-institutional, non-active duty military population. the national bureau of economic research (nber) provides sas, spss, and stata importation scripts to create a rectangular file (rectangular data means only person-level records; household- and family-level information gets attached to each person). to import these files into r, the parse.SAScii function uses nber's sas code to determine how to import the fixed-width file, then RSQLite to put everything into a schnazzy database. you can try reading through the nber march 2012 sas importation code yourself, but it's a bit of a proc freak show. this new github repository contains three scripts: 2005-2012 asec - download all microdata.R down load the fixed-width file containing household, family, and person records import by separating this file into three tables, then merge 'em together at the person-level download the fixed-width file containing the person-level replicate weights merge the rectangular person-level file with the replicate weights, then store it in a sql database create a new variable - one - in the data table 2012 asec - analysis examples.R connect to the sql database created by the 'download all microdata' progr am create the complex sample survey object, using the replicate weights perform a boatload of analysis examples replicate census estimates - 2011.R connect to the sql database created by the 'download all microdata' program create the complex sample survey object, using the replicate weights match the sas output shown in the png file below 2011 asec replicate weight sas output.png statistic and standard error generated from the replicate-weighted example sas script contained in this census-provided person replicate weights usage instructions document. click here to view these three scripts for more detail about the current population survey - annual social and economic supplement (cps-asec), visit: the census bureau's current population survey page the bureau of labor statistics' current population survey page the current population survey's wikipedia article notes: interviews are conducted in march about experiences during the previous year. the file labeled 2012 includes information (income, work experience, health insurance) pertaining to 2011. when you use the current populat ion survey to talk about america, subract a year from the data file name. as of the 2010 file (the interview focusing on america during 2009), the cps-asec contains exciting new medical out-of-pocket spending variables most useful for supplemental (medical spending-adjusted) poverty research. confidential to sas, spss, stata, sudaan users: why are you still rubbing two sticks together after we've invented the butane lighter? time to transition to r. :D

The LIDAR point cloud is an archive of hundreds of millions, or sometimes billions of highly accurate 3-dimensional x,y,z points and component attributes produced by the Environment Agency. The environment agecy site specific LIDAR DSM and DTM Time Stamped Tiles gridded raster products are derived from the point cloud. The component attributes a point cloud contains can provide valuable additional information to supplement elevation and can enable the user to make bespoke raster products such as canopy height models or intensity rasters. Site specific LIDAR surveys have been carried out across England since 1998, with certain areas, such as the coastal zone, being surveyed multiple times. The point cloud is available for surveys going back to 2006. Although the DSM and DTM Tile Stamped Tiles products are derived from the point cloud data there may not necessarily be a matching point cloud for each surface model due to historic data archiving processes. During processing the point cloud classifies the laser returns in the 'ground' and 'surface objects'. Further manual editing undertkaen on the derived digital terrain model (DTM) means the classifed ground points in the point cloud data will not match the final derived DTM. Data is available in 5km download zip files for each year of survey. Within each downloaded zip file are LAZ files aligned to the Ordinance Survey grid. The size of each tile is dependent upon the spatial resolution of the data. Please refere to the coverage metadata files for the start and end date flown of a survey as well as additional component information the point cloud contains such as the average point density. Attribution statement: © Environment Agency copyright and/or database right 2019. All rights reserved.

Time series of 40-year linear trend for the Arctic (ARC) and the Eastern Arctic (eARC) mean annual near surface air temperature (tas) for the period 1979-2100 as simulated by the CMIP5 models. The data for the period 1979-2005 are taken from the CMIP5 historical simulations, while data for 2006-2100 are from the future scenario rcp2.6, rcp4.5 and rcp8.5, respectively. The 40 year trend of 1979-2018 from the ERA-Interim reanalysis is also provided in separate files. The Arctic is defined as the area north of 70 N, and the eastern Arctic is defined as 0 - 180 E and north of 70 N. Each file contains the time series of the 40-year linear trend for the respective area mean tas from historical to one of the three scenarios simulated by one model. The linear trend is calculated using cdo, and the year associated with each data point in a file corresponding to the last year in the 40-year time period, e.g, the associated year 2018 corresponds to the linear trend calculated for the period 1979-2018. Unit: C/year.

Context

Time use can vary greatly by country and between genders, be it by it's location, cultural differences, or economic situation. The data provided is by no means exhaustive but contains some interesting information on leisure time by gender, as well as historic data (1950-2017) on Avg. work hours and GDP in different countries and continents.

Content

Datasets from two sources are provided: 1. OECD Time use country statistics: Based on a collection of different questionnaires for different countries, it provides a distribution for time spent on different activities for both men and women in different countries. 2. Penn World Table (PWT) with information on RGDPO (in mil. 2017US$), work hours and population (in millions) actively working. Covering 183 countries between 1950 and 2019.

*RGDPO: Output-side real GDP at chained PPPs, to compare relative productive capacity across countries and over time. Example: Productive capacity of China today compared to the US at some point in the past.

If you'd like, you can see an exploration of the data on my notebook: Data exploration

Acknowledgements

These databases provide additional indicators and may be of interest: - https://stats.oecd.org/Index.aspx?DataSetCode=TIME_USE - https://www.rug.nl/ggdc/productivity/pwt/

Inspiration

It is an interesting, easy to handle dataset which provides a great opportunity for interesting visuals and identifying relationships or trends between indicators.

Some questions to answer: - How to annual working hours relate to GDP per capita. - Is there a specific trend in working hours vs GDP per capita % change? Is it different for any specific region? - Is there any relationship between leisure time use and location, GDP or religion? - Is there a time use discrepancy by gender?

The Street Needs Assessment (SNA) is a survey and point-in-time count of people experiencing homelessness in Toronto on April 26, 2018. The results provide a snapshot of the scope and profile of the City's homeless population. The results also give people experiencing homelessness a voice in the services they need to find and keep housing. The 2018 SNA is the City's fourth homeless count and survey and was part of a coordinated point-in-time count conducted by communities across Canada and Ontario. The results of the 2018 Street Needs Assessment were summarized in a report and key highlights slide deck. During the course of the night, a 23 core question survey was completed with 2,019 individuals experiencing homelessness staying in shelters (including provincially-administered Violence Against Women shelters), 24-hour respite sites (including 24-hour women's drop-ins and the Out of the Cold overnight program open on April 26, 2018), and outdoors. The SNA includes individuals experiencing absolute homelessness but does not capture hidden homelessness (i.e., people couch surfing or staying temporarily with others who do not have the means to secure permanent housing). This dataset includes the SNA survey results; it does not include the count of people experiencing homelessness in Toronto. The SNA employs a point-in-time methodology for enumerating homelessness that is now the standard for most major US and Canadian urban centres. While a consistent methodology and approach has been used each year in Toronto, changes were made in 2018, in part, as a result of participation in the national and provincial coordinated point-in-time count. As a result, caution should be made in comparing these results to previous SNA survey results. Key changes included: administering the survey in a representative sample (rather than census) of shelters; administering the survey in all 24-hour respite sites and a sample of refugee motel programs added to the homelessness service system since the 2013 SNA; and a standard set of core survey questions that communities were required to follow to ensure comparability. In addition, in 2018, surveys were not conducted in provincially-administered health and treatment facilities and correctional facilities as was done in 2013. The 2018 survey results provide a valuable source of information about the service needs of people experiencing homelessness in Toronto. This information is used to improve the housing and homelessness programs provided by the City of Toronto and its partners to better serve our clients and more effectively address homelessness. Visit https://www.toronto.calcity-government/data-research-maps/research-reports/housing-and-homelessness-research-and-reports/

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South at 1:30 AM local time) or ascending (equatorial crossing South to North at 1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in thebox.

This dataset contains estimates of the prevalence of homelessness on Census night 2016, derived from the Census of Population and Housing using the Australian Bureau of Statistics (ABS) definition of homelessness. Prevalence is an estimate of how many people experienced homelessness at a particular point-in-time. Data is by SA2 2016 boundaries. Periodicity: 5 yearly. For more information visit the http://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/2049.0Explanatory%20Notes12016?OpenDocument' 'target='_blank' >Australian Bureau of Statistics. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2018): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

Field Data: The second ADCP data collection effort for this project was done on January 21, 2016 represented with these bathymetric point locations on the Green River near Tukwila, WA. The stream surface in the vicinity of the project area was relatively flat and water clarity was fair. Stream stage (water-level elevation) was 6.83 ft. higher relative to the July 2015 ADCP data collection effort. The upstream and downstream extent of the fishing site was identified by the Muckleshoot’s fisheries biologist Martin Fox to correspond with the tribes’ traditional fishing location. Reverse flow was first observed immediately along the right bank at the upstream end of the fishing site where the channel width slightly increases. This area of reverse flow was observed for approximately four meters (0.004 km) downstream where uniform flow returned across the stream channel. Due to the relatively high stage during the January 2016 data collection, a rubberized inflatable boat (R.I.B.) with a 15 horse-power outboard motor was used to collect the ADCP data. The transects measured on this date are not exactly the same as those measured in July 2015 due to the swift water conditions. As a result, multiple overlapping transects were measured to ensure high resolution XYZ point data that would be comparable to the July 2015 data. Transects measured here included a “zig-zag” transect that traverses multiple times between the upper and lower and near right and left bank extent, two parallel longitudinal transects in the immediate vicinity of the fishing site, one longitudinal transect along the left bank, and four parallel latitudinal transects covering the upper and lower extent of the fishing site. Processed Data: USGS provided the mean surface water elevation(or mean stage) collected at real-time streamgaging station No. 12113344 approximately 11 river miles upstream of the project surveyed site. The mean stage was estimated over the time period of ADCP data collection for this dataset and was used as the reference elevation for calculating the bed-elevation point data. Latitude, longitude and water depth were exported from the ADCP instrument using VMT (The Velocity Mapping Toolbox), a processing and visualization suite for moving-vessel ADCP measurements (Parsons, D.R., et al., 2013). These data are presented in the accompanying spreadsheet (GreenRiverBathymetry_20160121.csv) where the bed-elevation of the river bottom was calculated by subtracting the measured depth from the mean stage. An offset was applied to each measurement point in both the July and January datasets to correct for an error discovered during data post-processing, as explained in a comment within the attached spreadsheet.

This data set is part of an ongoing project to consolidate interagency fire perimeter data. The record is complete from the present back to 2020. The incorporation of all available historic data is in progress.The InFORM (Interagency Fire Occurrence Reporting Modules) FODR (Fire Occurrence Data Records) are the official record of fire events. Built on top of IRWIN (Integrated Reporting of Wildland Fire Information), the FODR starts with an IRWIN record and then captures the final incident information upon certification of the record by the appropriate local authority. This service contains all wildland fire incidents from the InFORM FODR incident service that meet the following criteria:Categorized as a Wildfire (WF) or Prescribed Fire (RX) recordIs Valid and not "quarantined" due to potential conflicts with other recordsNo "fall-off" rules are applied to this service.Service is a real time display of data.Warning: Please refrain from repeatedly querying the service using a relative date range. This includes using the “(not) in the last” operators in a Web Map filter and any reference to CURRENT_TIMESTAMP. This type of query puts undue load on the service and may render it temporarily unavailable.Attributes:ABCDMiscA FireCode used by USDA FS to track and compile cost information for emergency initial attack fire suppression expenditures. for A, B, C & D size class fires on FS lands.ADSPermissionStateIndicates the permission hierarchy that is currently being applied when a system utilizes the UpdateIncident operation.CalculatedAcresA measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire. More specifically, the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands. The minimum size must be 0.1.ContainmentDateTimeThe date and time a wildfire was declared contained. ControlDateTimeThe date and time a wildfire was declared under control.CreatedBySystemArcGIS Server Username of system that created the IRWIN Incident record.CreatedOnDateTimeDate/time that the Incident record was created.IncidentSizeReported for a fire. The minimum size is 0.1.DiscoveryAcresAn estimate of acres burning upon the discovery of the fire. More specifically when the fire is first reported by the first person that calls in the fire. The estimate should include number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.DispatchCenterIDA unique identifier for a dispatch center responsible for supporting the incident.EstimatedCostToDateThe total estimated cost of the incident to date.FinalAcresReported final acreage of incident.FinalFireReportApprovedByTitleThe title of the person that approved the final fire report for the incident.FinalFireReportApprovedByUnitNWCG Unit ID associated with the individual who approved the final report for the incident.FinalFireReportApprovedDateThe date that the final fire report was approved for the incident.FireBehaviorGeneralA general category describing the manner in which the fire is currently reacting to the influences of fuel, weather, and topography. FireCodeA code used within the interagency wildland fire community to track and compile cost information for emergency fire suppression expenditures for the incident. FireDepartmentIDThe U.S. Fire Administration (USFA) has created a national database of Fire Departments. Most Fire Departments do not have an NWCG Unit ID and so it is the intent of the IRWIN team to create a new field that includes this data element to assist the National Association of State Foresters (NASF) with data collection.FireDiscoveryDateTimeThe date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.FireMgmtComplexityThe highest management level utilized to manage a wildland fire event. FireOutDateTimeThe date and time when a fire is declared out. FSJobCodeA code use to indicate the Forest Service job accounting code for the incident. This is specific to the Forest Service. Usually displayed as 2 char prefix on FireCode.FSOverrideCodeA code used to indicate the Forest Service override code for the incident. This is specific to the Forest Service. Usually displayed as a 4 char suffix on FireCode. For example, if the FS is assisting DOI, an override of 1502 will be used.GACCA code that identifies one of the wildland fire geographic area coordination center at the point of origin for the incident.A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.IncidentNameThe name assigned to an incident.IncidentShortDescriptionGeneral descriptive location of the incident such as the number of miles from an identifiable town. IncidentTypeCategoryThe Event Category is a sub-group of the Event Kind code and description. The Event Category further breaks down the Event Kind into more specific event categories.IncidentTypeKindA general, high-level code and description of the types of incidents and planned events to which the interagency wildland fire community responds.InitialLatitudeThe latitude location of the initial reported point of origin specified in decimal degrees.InitialLongitudeThe longitude location of the initial reported point of origin specified in decimal degrees.InitialResponseDateTimeThe date/time of the initial response to the incident. More specifically when the IC arrives and performs initial size up. IsFireCauseInvestigatedIndicates if an investigation is underway or was completed to determine the cause of a fire.IsFSAssistedIndicates if the Forest Service provided assistance on an incident outside their jurisdiction.IsReimbursableIndicates the cost of an incident may be another agency’s responsibility.IsTrespassIndicates if the incident is a trespass claim or if a bill will be pursued.LocalIncidentIdentifierA number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year.ModifiedBySystemArcGIS Server username of system that last modified the IRWIN Incident record.ModifiedOnDateTimeDate/time that the Incident record was last modified.PercentContainedIndicates the percent of incident area that is no longer active. Reference definition in fire line handbook when developing standard.POOCityThe closest city to the incident point of origin.POOCountyThe County Name identifying the county or equivalent entity at point of origin designated at the time of collection.POODispatchCenterIDA unique identifier for the dispatch center that intersects with the incident point of origin. POOFipsThe code which uniquely identifies counties and county equivalents. The first two digits are the FIPS State code and the last three are the county code within the state.POOJurisdictionalAgencyThe agency having land and resource management responsibility for a incident as provided by federal, state or local law. POOJurisdictionalUnitNWCG Unit Identifier to identify the unit with jurisdiction for the land where the point of origin of a fire falls. POOJurisdictionalUnitParentUnitThe unit ID for the parent entity, such as a BLM State Office or USFS Regional Office, that resides over the Jurisdictional Unit.POOLandownerCategoryMore specific classification of land ownership within land owner kinds identifying the deeded owner at the point of origin at the time of the incident.POOLandownerKindBroad classification of land ownership identifying the deeded owner at the point of origin at the time of the incident.POOProtectingAgencyIndicates the agency that has protection responsibility at the point of origin.POOProtectingUnitNWCG Unit responsible for providing direct incident management and services to a an incident pursuant to its jurisdictional responsibility or as specified by law, contract or agreement. Definition Extension: - Protection can be re-assigned by agreement. - The nature and extent of the incident determines protection (for example Wildfire vs. All Hazard.)POOStateThe State alpha code identifying the state or equivalent entity at point of origin.PredominantFuelGroupThe fuel majority fuel model type that best represents fire behavior in the incident area, grouped into one of seven categories.PredominantFuelModelDescribes the type of fuels found within the majority of the incident area. UniqueFireIdentifierUnique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = POO protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters) FORIDUnique identifier assigned to each incident record in the FODR database.

This dataset contains estimates of the prevalence of homelessness on Census night 2011, derived from the Census of Population and Housing using the Australian Bureau of Statistics (ABS) definition …Show full descriptionThis dataset contains estimates of the prevalence of homelessness on Census night 2011, derived from the Census of Population and Housing using the Australian Bureau of Statistics (ABS) definition of homelessness. Prevalence is an estimate of how many people experienced homelessness at a particular point-in-time. Data is by LGA 2011 boundaries. Periodicity: 5 yearly. For more information visit the 'http://www.abs.gov.au/AUSSTATS/abs@.nsf/Lookup/2049.0Explanatory%20Notes12016?OpenDocument' 'target='_blank' >Australian Bureau of Statistics. Copyright attribution: Government of the Commonwealth of Australia - Australian Bureau of Statistics, (2018): ; accessed from AURIN on 12/16/2021. Licence type: Creative Commons Attribution 2.5 Australia (CC BY 2.5 AU)

Dataset organized by the Open-Earth-Monitor (OEMC) project within the context of Hackathon 2023.

The dataset contains monthly mean FAPAR values aggregated by each ground station. FAPAR represents the fraction of the incoming (photosynthetic active) radiation that is absorbed by vegetation, and is given in the range 0-1. It is a measure of vegetation health and ecosystem functioning, and a key parameter in light use efficiency models that model primary productivity.

For each monthly FAPAR value, a set of covariates / features were extracted from 32 raster spatial layers, including including satellite (spectral bands and indices) and temperature images (land surface temperature), climate images (precipitation) and digital terrain model (slope and elevation). The features are organized by columns, unique data points in time are identified by the sample_id column, and data points points belonging to the same location are identified by station_number.

Column names:

sample_id: unique identifier of datapoint

station: ground station number

fapar: monthly mean FAPAR

month: month of measurement

modis_{..}: NDVI, EVI, reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared) based on MOD13Q1

modis_lst_day_p{..}: Land surface temperatures daytime of percentiles 5th, 50th and 95th based on MOD11A2

modis_lst_night_p{..}: Land surface temperatures nighttime of percentiles 5th, 50th and 95th based on MOD11A2

wv_yearly_p{..}: Water vapour aggregated yearly by percentiles 25th, 50th and 75th based on derived from MCD19A2

wv_monthly_lt_p{..}: Water vapour aggregated long-term monthly by percentiles 25th, 50th and 75th based on MCD19A2

wv_monthly_lt_sd: Water vapour aggregated long-term monthly standard deviation based on MCD19A2

wv_monthly_ts_raw: Water vapour monthly time series based on MCD19A2

wv_monthly_ts_smooth: Water vapour monthly time series smoothed using the Whittaker method based on MCD19A2

accum_pr_monthly: Monthly accumulated precipitation based on CHELSA timeseries

dtm_{..}: Several DTM derivatives (Elevation, Slope, aspect (sine, cosine), curvature (up- and downslope), openness (negative, positive), compound topographic index (cti), valley bottom flatness (vbf)) based on MERIT DEM

Files

train.csv: Training set with 3,461 rows and 36 columns, including sample id (sample_id - index column), ground station (station), reference month (month), measured FAPAR (fapar), and 32 features / covariates

test.csv: Test set with 4,939 rows and 34 columns, including sample id (sample_id - index column), ground station (station), reference month (month) and 32 features / covariates

sample_submission.csv: a sample submission file with 4,939 rows and 2 columns, including sample id (sample_id - index column) and measured FAPAR (fapar)

The source of the data in this table are provinces and CBS offers them as a service as open data.

Statistics Netherlands (CBS) receives data from provinces in the context of the Third Party Information (Iv3) reports. The data in the table have not been edited by Statistics Netherlands. This type of data is also referred to as 'unprocessed data'. CBS bears no responsibility for the quality of the data. The data in Statistics Netherlands' own publications do not have to be traced back one-on-one to the data in this table.

The table contains raw Iv3 data from all reporting types of one reporting year. The types of reports are the budget, the four quarters and the annual accounts. If a province has not provided Iv3 data for a report type, then this province is included in the table, but each cell has the value '.' (missing).

The codes used in the table for the categories on the one hand and the task fields and balance sheet items on the other hand, as well as their meaning, are derived from the 'Decree on the budget and accountability of provinces and municipalities' (BBV) of the Ministry of the Interior and Kingdom Relations. The BBV contains, among other things, the regulations for the deliveries of Iv3 data to CBS.

For each type of report, all reports received so far are published at the same time at two points in time. The reason for placing the data a second time is that CBS gives the provinces the opportunity to provide an improved Iv3 dataset. The data that is placed the first time has the value '1st placement' in the topic 'Place'. The data that is placed the second time has the value '2nd placement'.

Data available from: 2023.

Status of figures The figures in this table are final upon publication (i.e., subject to exceptions, once published data are no longer updated).

Changes as of 10 April 2024: Figures for the second placement in Q4 2023 are included.

When will there be new figures? The time of publication of new figures for a type of report depends on the deadline for submission to Statistics Netherlands that applies to the type of report in question. For budgets for year j, the deadline for submission is 14 November in the year preceding the budget year (j-1). For quarterly data for the first, second and third quarters of year j, this is one month after the end of the quarter. For submission of the fourth quarter of year j, a deadline of 14 February in the year following the reporting year (j+1) applies. Finally, for the annual accounts for year j, this date is 14 July in the year following the reporting year (j+1). All reports received for a report type are published at the same time. This publication happens twice. The first time is 10 days after the submission deadline. If this day falls on the weekend or on a public holiday, the dates will be published on the next working day. With this placement, the most recent report received by each reporter will be published and received no later than 5 days after the deadline for submission. The second time is 70 days after the submission deadline. If this day falls on the weekend or on a public holiday, the dates will be published on the next working day. With this placement, the most recent report received by each reporter will be published and received no later than two months after the deadline for submission. The distinction between the first and the second placement can be seen in the subject.

On 19 June 2014, we conducted a two-tiered search (through that date) on the Web of Science Core Collection, CAB International, MEDLINE, Biological Abstracts, FSTA (Food Science and Technology Abstracts), and Zoological Record databases, using the ISI Web of Science search tool. We located 239,571 unique publications with the search terms: cotton OR Gossypium. A search of these records using the term “cover crop” resulted in 424 publications, composed of refereed articles, conference proceedings, research reports, and bulletins. With examination of these 424 eligible publications, 320 were excluded because they met our exclusion criteria: means for cover crop or no-cover crop treatments were not included, cotton yield or weed growth were not reported, article was a duplicate, article did not contain primary data (review or book), or they were not obtainable using interlibrary loan services (five articles). We did not include intercropping (cover crops grown simultaneously with cotton) studies, nor did we include studies that used weed count as the response variable. For the weed biomass effect size (ES), if an experiment included both weed and weed-free fallow no-cover-crop controls, we used the weed fallow no-cover-crop control in our analysis. If an experiment included herbicides applied over all treatments in season, we excluded the weed biomass ES but included the cotton biomass ES. We identified 104 articles that met our screening criteria (a full citation list and details of primary studies are provided in the supplemental material). Papers spanned 48 yr and were in English and Portuguese languages. Treatment means and number of replications (sample sizes) were collected for each study. For publications reporting means for more than one no-cover-crop (control) treatment in a nonfactorial experiment, we used the no-cover-crop control that most closely approximated the cover crop treatment. If replications were given as a range, we used the smallest value. For studies that did not report number of replications, we used n = 1 unless LSD or SEs were provided, in which case we used n = 2. If data were provided in graphical form, means were extracted using WebPlotDigitizer (Rogatgi, 2011). Multiple treatment combinations from one article were treated as independent studies (also referred to as trials or paired observations in the meta-analysis literature) and represented individual units in the meta-analysis. For example, Ashworth et al. (2018) and Li et al. (2013) examined the effects of two cover crop species over 3 yr, resulting in six studies from that article for lint yield ES. Vasilakoglou et al. (2011) studied control of three weed genera by four varieties of one cover crop species, resulting in 12 studies for the weed control ES. Although, the use of multiple studies from one publication has the disadvantage of increasing the dependence among studies that are assumed to be independent (Gurevitch and Hedges, 1999), the greater number of studies maximizes the meta-analysis’ statistical power (Lajeuness and Forbes, 2003). This approach has been used often in agricultural and plant biology meta-analyses (Mayerhofer et al., 2013; McGrath and Lobell, 2013; Ferraretto and Shaver, 2015). Therefore, we derived 1117 studies from 104 articles. As in prior meta-analyses (Ashworth et al., 2018; Mayerhofer et al., 2013), we used the final time point in the meta-analysis for studies that included data for multiple time points in one season. One exception was weed control, as an article used in this meta-analysis reported means that were recorded at three time points during the season (Norsworthy et al., 2010). Considering that each year of an experiment provides varying growing conditions only weakly correlated with other years (repeated measures across years is not needed in our experience), we considered each year as an independent study in the meta-analysis. Resources in this dataset:Resource Title: Meta analysis of cotton yield and weed suppression moderator data . File Name: Copy of Cotton cover crop meta data 12-15-14 heather victoria oz combined_hdt.xlsxResource Description: To systematically evaluate cover crop effects on cotton yield and weed suppression, we conducted a random-effects meta-analysis investigating 10 moderating variables in 104 articles, yielding 1117 independent studies over 48 yr.