The Hansen Global Forest Change version 1.7 datasets generated during and/or analysed during the current study are available in the earth engine partner’s website repository http://earthenginepartners.appspot.com/science-2013-global-forest. The datasets were developed by Hansen et al. (2013) in their paper "High-resolution global maps of 21st-century forest cover change". Science, 342 (6160), 850-853. https://doi.org/10.1126/science.1244693

The census of population in the Philippines, including the project populations, used in this study can be retrieved from the Philippine Statistics Authority (PSA) website https://psa.gov.ph/statistics/census/projected-population

The datasets were processed using an open source GIS software (QGIS version 3.16 Hannover) which can be downloaded from the QGIS website https://www.qgis.org/en/site/.

Payment Schedule Application - Cost Lists

This dataset provides information about an Entity, it's registered Agents and shares details

This dataset outlines the list of local courses offered by the Public Service Academy (PSA) during fiscal January 2023-December 2023 and the number of GoRTT officers trained in each course.

This dataset outlines the list of local courses offered by the Public Service Academy (PSA) during fiscal 2017-2018 and the number of GoRTT officers trained in each course.

This shapefile is based on a seabed sediment sample database of samples collected by INSS, INFOMAR and related surveys, including ADFish, DCU, FEAS, GATEWAYS, IMAGIN, IMES, INIS_HYRDO, JIBS, MESH, SCALLOP, SEAI, SEI, UCC. Where available, the results of particle size analysis are presented by displaying percentages of mud, sand and gravel fraction. Shapefile showing location of 4185 samples and samples can be colour coded in accordance with Folk sediment type classification for samples with available PSA (Particle Size Analysis) data. Shapefile showing location of 4185 samples and Folk classification for samples with available PSA (Particle Size Analysis) data.

A web map used for the Police Service Area Details web application.In addition to Police Districts, every resident lives in a Police Service Area (PSA), and every PSA has a team of police officers and officials assigned to it. Residents should get to know their PSA team members and learn how to work with them to fight crime and disorder in their neighborhoods. Each police district has between seven and nine PSAs. There are a total of 56 PSAs in the District of Columbia.Printable PDF versions of each district map are available on the district pages. Residents and visitors may also access the PSA Finder to easily locate a PSA and other resources within a geographic area. Just enter an address or place name and click the magnifying glass to search, or just click on the map. The results will provide the geopolitical and public safety information for the address; it will also display a map of the nearest police station(s).Each Police Service Area generally holds meetings once a month. To learn more about the meeting time and _location in your PSA, please contact your Community Outreach Coordinator. To reach a coordinator, choose your police district from the list below. The coordinators are included as part of each district's Roster.Visit https://mpdc.dc.gov for more information.

Data Source: U.S. Census Bureau, American Community Survey (ACS) 5-year Estimates Special Tabulation on Aging and Disability 2016-2020.
*Note. The total population only includes individuals for whom the poverty status is determined, excluding institutionalized group quarter populations (e.g., college dormitories, military housing).

In response to COVID-19, regional Area Agencies on Aging (AAA) and their sub-contractors have been providing additional Information & Assistance calls to connect older adults, adults with disabilities, families, and caregivers to services in the community.

This is a method for tracking data updates for our PSA project.

The dataset contains polygons representing of MPD Police Sectors, 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. In 2017 the Metropolitan Police Department formed an additional operational geographic layer called Sector. The Sector model brings additional management accountability to districts and allows for faster dispatch, lower response times, and improved service to the community. Sectors are made up of multiple Police Service Areas (PSAs) and are headed by a Captain. Please note that PSA is still an active operational model used by MPD; Sector is an additional layer between the PSA and District levels.2019 Boundary Changes:Periodically, MPD conducts a comprehensive assessment of our patrol boundaries to ensure optimal operations. This effort considers current workload, anticipated population growth, economic development, and community needs. The overarching goals for the 2019 realignment effort included: optimal availability of police resources, officer safety and wellness, and efficient delivery of police services. These changes took effect on 01/10/2019.On 03/27/2019, this boundary was modified to adjust dispatching of North Capitol Street’s northwest access roads to be more operationally efficient.

Metropolitan Police Department (MPD) Police Service Areas (PSA). The dataset contains polygons representing of MPD PSA, 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. Police jurisdictions were initially created selecting street arcs from the planimetric street centerlines and street polygons, water polygons, real property boundaries and District of Columbia boundaries.2019 Boundary Changes:Periodically, MPD conducts a comprehensive assessment of our patrol boundaries to ensure optimal operations. This effort considers current workload, anticipated population growth, development, and community needs. The overarching goals for the 2019 realignment effort included: optimal availability of police resources, officer safety and wellness, and efficient delivery of police services. These changes took effect on 01/10/2019. On 03/27/2019, this boundary was modified to adjust dispatching of North Capitol Street’s northwest access roads to be more operationally efficient.

Data Source: U.S. Census Bureau, American Community Survey (ACS) 5-year Estimates Special Tabulation on Aging and Disability 2016-2020.
*Note. People who are actively working or are not currently working but have recently and would like to work are considered in the labor force. Those who have never worked or are retired are not in the labor force.

1Originally single data for a single port, then transformed to pairwise using the formula a+b where a and b are single values for each port.2Calculated from satellite images using the "ruler" function of Google Earth.3Official data from the Philippine Statistics Authority—National Statistics Office (http://web0.psa.gov.ph/).4Official data from the Philippine Ports Authority (http://www.pdosoluz.com.ph/).Definitions of the variables used to test the hypothesis that human transportation influences population structure of Ae. aegypti in the central-western Philippines.

Abstract

The 2022 Philippines National Demographic and Health Survey (NDHS) was implemented by the Philippine Statistics Authority (PSA). Data collection took place from May 2 to June 22, 2022.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, fertility preferences, family planning practices, childhood mortality, maternal and child health, nutrition, knowledge and attitudes regarding HIV/AIDS, violence against women, child discipline, early childhood development, and other health issues.

The information collected through the NDHS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the country’s population. The 2022 NDHS also provides indicators anchored to the attainment of the Sustainable Development Goals (SDGs) and the new Philippine Development Plan for 2023 to 2028.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the 2022 NDHS was based on a two-stage stratified sample design using the Master Sample Frame (MSF) designed and compiled by the PSA. The MSF was constructed based on the listing of households from the 2010 Census of Population and Housing and updated based on the listing of households from the 2015 Census of Population. The first stage involved a systematic selection of 1,247 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 22 or 29 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the preselected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15–49 who were either usual residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on women’s safety.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires were used for the 2022 NDHS: the Household Questionnaire and the Woman’s Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, academe, and international agencies. The survey protocol was reviewed by the ICF Institutional Review Board.

After all questionnaires were finalized in English, they were translated into six major languages: Tagalog, Cebuano, Ilocano, Bikol, Hiligaynon, and Waray. The Household and Woman’s Questionnaires were programmed into tablet computers to allow for computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the languages for each questionnaire.

Cleaning operations

Processing the 2022 NDHS data began almost as soon as fieldwork started, and data security procedures were in place in accordance with confidentiality of information as provided by Philippine laws. As data collection was completed in each PSU or cluster, all electronic data files were transferred securely via SyncCloud to a server maintained by the PSA Central Office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the area of assignment. Timely generation of field check tables allowed for effective monitoring of fieldwork, including tracking questionnaire completion rates. Only the field teams, project managers, and NDHS supervisors in the provincial, regional, and central offices were given access to the CAPI system and the SyncCloud server.

A team of secondary editors in the PSA Central Office carried out secondary editing, which involved resolving inconsistencies and recoding “other” responses; the former was conducted during data collection, and the latter was conducted following the completion of the fieldwork. Data editing was performed using the CSPro software package. The secondary editing of the data was completed in August 2022. The final cleaning of the data set was carried out by data processing specialists from The DHS Program in September 2022.

Response rate

A total of 35,470 households were selected for the 2022 NDHS sample, of which 30,621 were found to be occupied. Of the occupied households, 30,372 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 28,379 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 27,821 women, yielding a response rate of 98%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Philippines National Demographic and Health Survey (2022 NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 NDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Population pyramid
• Five-year mortality rates

See details of the data quality tables in Appendix C of the final report.

Austin Energy’s Power Supply Adjustment recovers fuel for generation, transportation, renewable purchase power agreements, purchase power to serve retail customers, ERCOT fees, hedging and the balance from the previous period. The adjustment is reviewed annually. Find more information at http://austinenergy.com/go/reports.

View metadata for key information about this dataset.The PSA boundaries replaced a much smaller boundary, Sectors in 2009. In several Districts, PSA's split Sector boundaries and therefore a historical comparison would not necessarily be accurate. These polygon features were created by the Crime Mapping & Analysis Unit and they represent an initiative by the Philadelphia Police Department that started in 2009. A Police District can have up to 4 PSA's and as few as 2. There is a Police Lieutenant assigned to each PSA and officers are limited to patrol their specific boundary with the goal of familiarity with the area residents and businesses.See also the related datasets:Police DistrictsPolice DivisionsFor questions about this dataset, contact publicsafetygis@phila.gov. For technical assistance, email maps@phila.gov.

This data set presents key demographic characteristics of Californians Age 60 and Over. This data set can be viewed by county or Area Agency on Aging Planning and Services Area. Key sociodemographic variables include: lives alone, low income, minority/non-minority, non-English speaking, and living in a rural area. This data is based on multiple federal and state sources.

Data Source: U.S. Census Bureau, American Community Survey (ACS) 5-year Estimates 2016-2020 .
*Note. An occupied housing unit includes a house, an apartment, a mobile home or trailer, a group of rooms, or a single room that is occupied as separate living quarters. It excludes group quarters, such as residence halls, residential treatment centers, skilled nursing facilities, group homes, military barracks, correctional facilities, and workers' dormitories.

This data set represents the total number of Californians age 60 and over who were provided a meal from the Older Americans Act Title IIIC-1 Nutrition Services Program – Congregate Meals. Key sociodemographic variables include: age, high risk nutrition status, low income, lives alone and minority/non-minority.