4 datasets found
  1. H

    Gridded Land Surface Temperature and Selected Environmental and...

    • dataverse.harvard.edu
    Updated May 5, 2024
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    Homer PAGKALINAWAN; Sharon Feliza Ann MACAGBA; Laurence DELINA (2024). Gridded Land Surface Temperature and Selected Environmental and Socioeconomic Features of Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta [Dataset]. http://doi.org/10.7910/DVN/PIR5GI
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 5, 2024
    Dataset provided by
    Harvard Dataverse
    Authors
    Homer PAGKALINAWAN; Sharon Feliza Ann MACAGBA; Laurence DELINA
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Time period covered
    Feb 1, 2000 - Dec 31, 2023
    Area covered
    Metro Manila, Jakarta Metropolitan Area, Bangkok Metropolitan Area
    Description

    The dataset provides monthly gridded LST from MODIS’ MOD21 product in February 2000 to December 2023 for Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta. We also collected are selected environmental and socioeconomic variables for each grid. This includes building and built-up areas, areas of greeneries, industrial zones, and water bodies, night-time light (to approximate areas of economic activities), gridded population, distance from water bodies, and indicators on presence of roads and airports. We also derived an indicator that determines clusters of neighboring grids with high value of temperature. Lastly, we identified which administrative units, i.e. barangays for Metropolitan Manila, tambong for Bangkok Metropolitan Area, and kampong for Greater Jakarta, have at least of 50% of their land area within the mapped extreme heat clusters.

  2. Average daily traffic volume Metro Manila Philippines 2024, by vehicle

    • statista.com
    Updated Apr 2, 2025
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    Statista (2025). Average daily traffic volume Metro Manila Philippines 2024, by vehicle [Dataset]. https://www.statista.com/statistics/1262359/philippines-average-daily-traffic-metro-manila-by-vehicle-type/
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    Dataset updated
    Apr 2, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Philippines
    Description

    In 2024, motorcycles contributed the highest average daily traffic volume in Metro Manila in comparison to other types of vehicles. In that year, the traffic volume of motorcycles reached around 1.9 million. This was followed by the volume of cars on the road.

  3. i

    Family Income and Expenditure Survey 1991 - Philippines

    • dev.ihsn.org
    • catalog.ihsn.org
    • +1more
    Updated Apr 25, 2019
    + more versions
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    National Statistics Office (2019). Family Income and Expenditure Survey 1991 - Philippines [Dataset]. https://dev.ihsn.org/nada/catalog/study/PHL_1991_FIES_v01_M
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    Dataset updated
    Apr 25, 2019
    Dataset authored and provided by
    National Statistics Office
    Time period covered
    1991 - 1992
    Area covered
    Philippines
    Description

    Abstract

    The 1991 Family Income and Expenditure Survey (FIES) is a nationwide survey of households undertaken by the National Statistics Office (NSO). Similar surveys were conducted in 1956-1957, 1961, 1965, 1971, 1975, 1979, 1985 and 1988. Like the previous surveys, this undertaking aims to accomplish the following primary objectives:

    1. to gather data on family income and family living expenditures and related information affecting income and expenditure levels and patterns in the Philippines;

    2. to determine the sources of income and income distribution, levels of living and spending patterns, and the degree of inequality among families;

    3. to provide benchmark information to update weights for the estimation of consumer price index (CPI)

    Geographic coverage

    National coverage

    Analysis unit

    Household Consumption expenditure item Income by source

    Universe

    The 1991 FIES has as its target population, all households and members of households nationwide. A household is defined as an aggregate of persons, generally but not necessarily bound by ties of kinship, who live together under the same roof and eat together or share in common the household food. Household membership comprises the head of the household, relatives living with him such as his/her spouse, children, parent, brother/sister, son-in-law/daughter-in-law, grandson/granddaughter and other relatives. Household membership likewise includes boarders, domestic helpers and non-relatives. A person who lives alone is considered a separate household.

    Institutional population is not within the scope of the survey.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sampling design of the 1991 Family Income and Expenditures Survey (FIES) adopts that of the Integrated Survey of Households (ISH), of the National Statistics Office (NSO) which uses a stratified two-stage cluster sampling design with the population size of the barangay as the stratifying variable.

    The urban and rural areas of each province are the principal domains of the survey. In addition, the urban and rural areas of cities with a population of 150,000 or more as of 1980 are also made domains of the survey. These cities are the 4 cities in Metro Manila (Manila, Quezon City, Pasay and Caloocan), and the cities of Angeles, Olongapo, Bacolod, Iloilo, Cebu, Zamboanga, Butuan, Cagayan de Oro, Davao and IIigan.

    The rest of Metro Manila, i.e., Makati, Pasig and the 11 other municipalities are treated as three separate domains. In the case of Makati, six exclusive villages are identified and samples are selected using a different scheme. These villages are Forbes Park, Bel-Air, Dasmarinas, San Lorenzo, Urdaneta and Magallanes.

    In general, the sample design results in a self-weighting samples within domains, with a uniform sampling fraction of 1:400 for urban areas and 1:6000 for rural areas. However, special areas are assigned different sampling fractions so as to obtain adequate samples for each. Special areas refer to the urban or rural areas of a province or large city which are small relative to their counterparts. T

    The primary sampling units (PSUs) under the sample design are the barangays and the households within each sample barangay comprise the secondary sampling units (SSUs). For the purpose of selecting the PSUs, the barangay in each domain are arranged by population size (as of the 1980 Census of Population) in descending equal sizes. Four independent PSUs are drawn with probability proportional to size with complete replacement.

    Within each PSU selected at the first stage, a pre-determined number of households (i.e. SSU's) is selected at the second stage using a systematic selection procedure with a random start. The number of households chosen from the ith PSU takes into account the probability of selecting the PSU at the first stage such that each household within the domain has the same over-all probability of selection for the survey (i.e. the sample was self-weighting within domains).

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The questionnaire has five main parts consisting of the following: Part I. Identification and Other Information (Geographic Identification, Other Information and Particulars about the Family)

    Part II. Expenditures and Other Disbursements Section A. Food, Alcoholic Beverages and Tobacco Section B. Fuel, Light and Water, Transportation and Communication, Household Operations Section C. Personal Care and Effects, Clothing Footwear and Other Wear Section D. Education, Recreation, and Medical Care Section E. Furnishings and Equipment Section F. Taxes Section G. Housing, House Maintenance and Minor Repairs Section H. Miscellaneous Expenditures Section I. Other Disbursements

    Part III. Income and Other Receipts Section A. Salaries and Wages from Employment Section B. Net Share of Crops, Fruits and Vegetables Produced or Livestock and Poultry Raised by Other Households Section C. Other Sources of Income Section D. Other Receipts Section E. Check List for Family Sustenance Activities

    Part IV. Entrepreneurial Activities Section A1. Crop Farming and Gardening Section A2. Livestock and Poultry Raising Section A3. Fishing Section A4. Forestry and Hunting Section A5. Wholesale and Retail Section A6. Manufacturing Section A7. Community, Social, Recreational and Personal Services Section A8. Transportation, Storage and Communication Services Section A9. Mining and Quarrying Section A10. Construction Section A11. Entrepreneurial Activities Not Elsewhere Classified

    Cleaning operations

    The 1991 FIES questionnaire contains about 800 data items and a guide for comparing income and expenditures and internal consistency.

    Upon submission of the data diskettes containing first and second visit data, a summary file was extracted from the entire file through a computer program. This summary file provided the basis for the generation of the preliminary results in August of 1992.

    The questionnaires were further subjected to a rigorous manual and machine edit checks for completeness, arithmetic accuracy, range validity and internal consistency. Items failing any of the edit checks were either corrected automatically by the computer on the basis of pre-determined specifications or, when needed, examined in a clerical error-reconcillation operation.

    The electronic data-processing (EDP) system developed by the NSO Data Processing Staff and used in the 1985 and 1988 FIES was generally adopted in processing the 1991 FIES with few modifications. There are thirteen (13) major steps in the machine processing of the 1991 FIES and these are as follows: 1. Data entry and verification 2. Structural editing (minor edit) 3. Edit list verification/correction 4. Update 5. Completeness check 7. Identification verification 8. Extraction of summary file for preliminary results 9. Matching of visit records (big edit) 10. Expansion 11. Tabulation 12. Generation of CPI weight tables 13. Variance analysis

    Steps 1 to 8 were performed right after each visit while the remaining steps were carried out upon completion of the data collection for the first and second visits. Steps 1 to 7 were implemented at the regional office while the concluding steps were handled by the Central Office.

    For data entry, IMPS (Integrated Microcomputer Processing System) was used.

    Response rate

    The response rate is the ratio of the total responding households to the total number of eligible households. Eligible households include households who were completely interviewed, refused to be interviewed or were temporarily away or not at home or on vacation during the survey period.

    Sampling error estimates

    As in all surveys, two types of non-response were encountered in the 1991 FIES: interview non-response and item non-response. Interview non-response refers to a sample household that could not be interviewed. Since the survey requires that the sample households be interviewed in both visits, households that transferred to another dwelling unit, temporarily away, on vacation, not at home, household unit demolished, destroyed by fire/typhoon and refusal to be interviewed in the second visit contributed to the number of interview non-response cases.

    Item non-response, or the failure to obtain responses to particular survey items, resulted from factors such as respondents being unaware of the answer to a particular question, unwilling to provide the requested information or ENs' omission of questions during the interview. Deterministic imputation was done to address item nonresponse. This imputation is a process in which proper entry for a particular missing item was deduced from other items of the questionnaire where the non-response item was observed. Notes and remarks indicated in the questionnaire were likewise used as basis for imputation.

  4. Popular modes of transportation Philippines 2023

    • statista.com
    Updated Jun 23, 2025
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    Statista (2025). Popular modes of transportation Philippines 2023 [Dataset]. https://www.statista.com/statistics/1338717/philippines-most-used-modes-of-transportation/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2023
    Area covered
    Philippines
    Description

    According to a consumer survey in the Philippines as of January 2023, approximately ** percent of respondents used public transportation such as trains and buses as their main mode of transportation. This was followed by ** percent of respondents who preferred to use private motorcycles.

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Homer PAGKALINAWAN; Sharon Feliza Ann MACAGBA; Laurence DELINA (2024). Gridded Land Surface Temperature and Selected Environmental and Socioeconomic Features of Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta [Dataset]. http://doi.org/10.7910/DVN/PIR5GI

Gridded Land Surface Temperature and Selected Environmental and Socioeconomic Features of Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
May 5, 2024
Dataset provided by
Harvard Dataverse
Authors
Homer PAGKALINAWAN; Sharon Feliza Ann MACAGBA; Laurence DELINA
License

CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically

Time period covered
Feb 1, 2000 - Dec 31, 2023
Area covered
Metro Manila, Jakarta Metropolitan Area, Bangkok Metropolitan Area
Description

The dataset provides monthly gridded LST from MODIS’ MOD21 product in February 2000 to December 2023 for Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta. We also collected are selected environmental and socioeconomic variables for each grid. This includes building and built-up areas, areas of greeneries, industrial zones, and water bodies, night-time light (to approximate areas of economic activities), gridded population, distance from water bodies, and indicators on presence of roads and airports. We also derived an indicator that determines clusters of neighboring grids with high value of temperature. Lastly, we identified which administrative units, i.e. barangays for Metropolitan Manila, tambong for Bangkok Metropolitan Area, and kampong for Greater Jakarta, have at least of 50% of their land area within the mapped extreme heat clusters.

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