Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Pakistan data available from WorldPop here.

The territories of Pakistan and India are mostly covered by the non-political blocks AS42 through AS50, going roughly from West to East. Please see the attached map of these non-political boundary blocks.

The territories of Pakistan and India are mostly covered by the non-political blocks AS42 through AS50, going roughly from West to East. Please see the attached map of these non-political boundary blocks.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM_popmap10adj_v2.tif = Vietnam (VNM) population count map for 2010 (popmap10) adjusted to match UN national estimates (adj), version 2 (v2). DATE OF PRODUCTION: January 2013

The raster dataset consists of a 500m score grid for slaughterhouse industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on cattle production intensification potential defined using crop production, livestock production systems, and cattle distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, cattle distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility). It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.2) + (”Cattle intensification” * 0.3) + (“Poverty” * 0.1).

The raster dataset consists of a 500m score grid for cotton storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Cotton. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.1) + (“Poverty” * 0.1) + ("Major Ports Accessibility" * 0.1)+("Major Regional Cities Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

Tariff Structure announced by Government of Pakistan.

The raster dataset consists of a 500m score grid for temperate fruits storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Temperate fruits. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.1) + (“Poverty” * 0.1) + ("Major Ports Accessibility" * 0.1)+("Major Regional Cities Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

The raster dataset consists of a 500m score grid for wheat storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Wheat. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.1) + (“Poverty” * 0.1) + ("Major Ports Accessibility" * 0.1)+("Major Regional Cities Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

Abstract

The survey has been conducted with the aim to provide data for use by the government in formulating the poverty reduction strategy as well as development plans at district level and rapid assessment of programs initiated under Poverty Reduction Strategy Paper and Medium Term Development Framework (MTDF) in the overall context of MDG. The survey provides indicators on Education Health, Population, Welfare, Income and Expenditure, Agricultural and Non-agricultural Activity, Shocks and crises.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual

Universe

The universe of this survey consists of all urban and rural areas of all four provinces, from the scope of the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING FRAME

Urban area: FBS has developed its own urban area frame. All urban areas comprising cities/ towns have been divided into small compact areas known as enumeration blocks (E.Bs) identifiable through map. Each enumeration block comprises about 200-250 households and categorized into low, middle and high-income group, keeping in view the socio economic status of the majority of households. Urban area sampling frame consists of 26698 enumeration blocks has been updated in 2003.

Rural area: With regard to the rural areas, the lists of villages/mouzas/deh according to Population Census, 1998 have been used as sampling frame. In this frame, each village/mouzas/deh is identifiable by its Name, Had Bast Number, Cadastral map etc. This frame is comprised 50590 villages/mouzas

STRATIFICATION PLAN Urban Areas: Within each district large sized cities having population five lack and above have been treated as independent stratum. Each of these cities has further been sub-stratified into low, middle and high group’s areas. The remaining cities/towns within each district have been grouped together to constitute an independent stratum.

Rural Areas: The entire rural domain of a district for Punjab, Sindh, NWFP and Balochistan provinces has been considered as independent stratum.

Sample Size and its Allocation: To determine optimum sample size for this survey, analytical studies based on the results of Pakistan Demographic Survey, Labour Force and Pakistan Integrated Households Sample Survey were undertaken. Keeping in view the variability exist within the population for the characteristics for which estimates are to be prepared, population distribution, level of estimates and field resources available a sample size of 77488 households enumerated from 5413 sample PSUs (2280 from urban and 3133 from rural areas) has been considered sufficient to produce reliable estimates at district level in respect of all provinces.

Sample Design: A two-stage Stratified Random Sampling scheme was adopted for this survey. Enumeration Blocks in urban areas and villages in rural areas were selected at first stage while households within the sample Enumeration Blocks / Villages were selected at second stage.

Selection of primary sampling Units (PSUs): Enumeration blocks in the urban domain and mouzas/deh/villages in rural domain have been taken as primary sampling units (PSUs). In urban domain sample PSUs from each stratum have been selected by probability proportional to size (PPS) method of sampling scheme using households in each block as measure of size (MOS). Similarly in rural areas, population of each village has taken as MOS for selection of sample villages using probability proportional to size method of selection.

Selection of Secondary Sampling Units (SSUs): Households within each sample Primary Sampling Unit (PSU) have been considered as Secondary Sampling Units (SSUs). 16 and 12 households have been selected from each sample village and enumeration block respectively by systematic sampling scheme with a random start.

Mode of data collection

Face-to-face [f2f]

The raster dataset consists of a 500m score grid for dairy processing industry (UHT and milk powder) facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on cattle dairy production intensification potential defined using crop production, livestock production systems, and cattle distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, cattle distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) 4. Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.25) + ("Human Population Density" * 0.1) + (“Major Cities Accessibility” * 0.1) + (“ Accessibility to ports” * 0.1) + ( "Poverty" * 0.1) + (”Dairy Intensification” * 0.35).

The raster dataset consists of a 500m score grid for slaughterhouse industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on goat production intensification potential defined using crop production, livestock production systems, and goat distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, goat distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility). It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.2) + (”Goat intensification” * 0.3) + (“Poverty” * 0.1)

The raster dataset consists of a 500m score grid for vegetables storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Vegetables. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.1) + (“Poverty” * 0.1) + ("Major Ports Accessibility" * 0.1)+("Major Regional Cities Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

The research on the vulnerability dataset of disaster bearing bodies in the China Pakistan Economic Corridor (domestic section) is based on multi-source data fusion, and a vulnerability evaluation system covering natural disasters and socio-economic systems has been constructed. This dataset integrates field survey data (infrastructure distribution, population density), satellite remote sensing data (surface deformation monitoring, vegetation coverage), and statistical yearbook data (GDP, disaster prevention investment), and forms a multidimensional vulnerability database through GIS spatial analysis, remote sensing interpretation, and data standardization processing. The research team has developed a three-dimensional evaluation index system that includes exposure, sensitivity, and adaptability. The exposure index covers physical elements such as the proportion of geological hazard prone areas and the density of transportation arteries; Sensitivity indicators involve socio-economic factors such as ecological vulnerability index and poverty incidence rate; The indicators of adaptability include emergency response capability, medical resource density, and other elements of disaster prevention and reduction capability. To improve the evaluation accuracy, the traditional vulnerability index model was improved by introducing the random forest algorithm for weight optimization, and the stability of the model was verified through Monte Carlo simulation. The analysis results show that there is significant spatial heterogeneity in the domestic section of the corridor: high vulnerability areas are concentrated in the Karakoram Pamir geologically active zone, driven by a combination of frequent extreme weather events, insufficient infrastructure disaster resistance standards, and weak regional economic resilience. The future research can be further extended to the high-altitude mountains along the "the Belt and Road". In combination with multi-scale remote sensing monitoring and socio-economic big data, we can deepen the research on the formation mechanism of cross-border disaster risk in the context of climate change, and provide scientific support for building a resilient Silk Road.

The 500m raster dataset represents selected top location score areas filtered by exclusive criteria: access to finance, distance to major roads, access to IT and distance to urban areas. The layer was produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location.

The location score is achieved by processing sub-model outputs characterizing logistical factors dairy processing facilities siting: Supply, demand, Infrastructure/accessibility. The top 99th percentile is selected/clipped using the exclusive criteria.

Access to finance, distance to roads and urban areas are defined using a linear distance threshold: • Banks - approx. 20km (0.18 degree) buffer radius. • Major roads - approx. 2km (0.018 degree) buffer radius. Access to IT is characterized applying the mobile broadband coverage map.

Data publication: 2021-10-18

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Dariia Nesterenko

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets: 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2). 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. Poverty: VMPI source: UNDP (United Nations Development Programme). 7. Mobile Broadband Coverage produced based on: Coverage Data © Collins Bartholomew and GSMA 2021.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped raster TIF file for Dairy Processing Final Location: Goat (Pakistan - ~ 500m)

The raster dataset consists of a 500m score grid for tropical fruits storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Tropical fruits. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.1) + (“Poverty” * 0.1) + ("Major Ports Accessibility" * 0.1)+("Major Regional Cities Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

The raster dataset consists of a 500m score grid for slaughterhouse industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on buffalo production intensification potential defined using crop production, livestock production systems, and buffalo distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, buffalo distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.2) + (”Buffalo Intensification” * 0.3) + (“Poverty” * 0.1)

The raster dataset consists of a 500m score grid for rice storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Rice. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.1) + (“Poverty” * 0.1) + ("Major Ports Accessibility" * 0.1)+("Major Regional Cities Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

The raster dataset consists of a 500m score grid for dairy processing industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on goat dairy production intensification potential defined using crop production, livestock production systems, and goat distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, goat distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) 4. Poverty. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.25) + ("Human Population Density" * 0.1) + (“Major Cities Accessibility” * 0.1) + (“ Accessibility to ports” * 0.1) + ( "Poverty" * 0.1) + (”Dairy Intensification” * 0.35).

Abstract

This round of the HIES was conducted covering 15807 households. It provides important information on household income, savings, liabilities, and consumption expenditure and consumption patterns at national and provincial level with urban/rural breakdown. It also includes the requisite data on consumption for the Planning & Development Division for estimation of poverty.

The data generated though HIES Survey will be used to assist the government in formulating the poverty reduction strategy in the overall context of MDGs. The indicators will be developed at National/Provincial level in the following sectors. 1. Education 2. Health 3. Water Supply & Sanitation. 4. Population Welfare 5. Income & Expenditure

Universe

The universe of this survey consists of all urban and rural areas of all four provinces. Military restricted and protected areas have been excluded from the scope of the survey.

Sampling procedure

Sampling Frame:

Urban area: PBS has developed its own urban area frame. All urban areas comprising cities/ towns have been divided into small compact areas known as enumeration blocks (E.Bs) identifiable through map. Each enumeration block comprises about 200-250 households and categorized into low, middle and high-income group, keeping in view the socioeconomic status of the majority of households. Urban area sampling frame consists of 26698 enumeration blocks has been updated in 2003. Rural area: With regard to the rural areas, the lists of villages/mouzas/dehs according to Population Census, 1998 have been used as sampling frame. In this frame, each village/mouza/deh is identifiable by its Name, Had Bast Number, Cadastral map etc. This frame is comprised of 50590 villages/mouzas.

Stratification Plan

Urban Areas: Large sized cities having population five laces and above have been treated as independent stratum. Each of these cities has further been sub-stratified into low, middle and high income groups. The remaining cities/towns within each defunct administrative division have been grouped together to constitute an independent stratum. Rural Areas: The entire rural domain of a district for Punjab, Sindh and KPK provinces has been considered as independent stratum, whereas in Balochistan province defunct administrative division has been treated as stratum.

Sample Size and its Allocation:

To determine optimum sample size for this survey, analytical studies based on the results of Pakistan Demographic Survey, Labour Force and Pakistan Integrated Households Sample Survey were undertaken. Keeping in view the variability that exists within the population for the characteristics for which estimates are to be prepared, as well as population distribution, reliability of estimates and field resources available a sample of size 17,056 households distributed over 1217 PSUs (604 urban and 613 rural) has been considered sufficient to produce reliable estimates in respect of all provinces. Out of these 1217 PSUs, 59 PSUs (19 urban and 40 rural PSUs) were dropped and the remaining 1158 PSUs (585 urban and 573 rural) comprising 15807 households were covered.

Sample Design: A two-stage stratified sample design has been adopted for this survey.

Selection of primary sampling Units (PSUs): Enumeration blocks in the urban domain and mouzas/dehs/villages in rural domain have been taken as PSUs. In urban domain sample PSUs from each stratum have been selected by PPS method of sampling scheme; using households in each block as MOS. Similarly in rural areas, the population of each village has been taken as MOS for the selection of sample villages using again the PPS method.

Selection of Secondary Sampling Units (SSUs): Households within PSU have been considered as SSUs. 16 and 12 households have been selected from each sample village and enumeration block respectively by systematic sampling scheme with a random start.

Mode of data collection

Face-to-face [f2f]

Research instrument

The household income and consumption part of the HIES questionnaire is the same which has been used for the previous rounds since 2001-02 however, some minor improvements have been made for the reference year.

The main structure of the HIES questionnaire used for the survey 2011-12 is as shown:-

Section A: Survey Information Section1: Part-A: Household Information Part-B: Employment & Income Section 2: Education Section 3: Part-A: Darrhoea Part-B: Immunisation Part-C: Malaria & Tuberculosis Section 4: Part-A: Pregnancy History Part-B: Maternity History Part-C Family Planning Part-D: Pre & Post Natal Care Part-E: Women in Decision Making Section 5: Housing Consumption Module Section 6: Household Consumption Expenditure Section 7: Selected Durable Consumption Items Owned/Sold by the Household (During Last One Year) Section 8: Transfers Received and Paid Out (During Last One Year) Section 9: Part- A: Buildings and Land Owned by Members of This Household..... Part- B: Financial Assets And Liabilities, Loans And Credit Section 10: Part A: Agricultural Sheet Part B: Livestock,Poultry,Fish,Forestry,Honey Bee Section 11 :Non-Agricultural Establishment Section 12: Balance Sheet for Income and Expenditure

Data appraisal

Data quality in the HIES Survey has been ensured through a built in system of checking of field work by the supervisors in the field as well as teams from the headquarters. Regional/ Field offices ensured the data quality through preliminary editing at their office level. The entire data entry was carried at the PBS headquarter in Islamabad and the data entry programme used had a number of in built consistency checks