This statistic shows the biggest cities in Pakistan as of 2023. In 2023, approximately ***** million people lived in Karāchi, making it the biggest city in Pakistan.

Major Cities in Pakistan by Population

Pakistani Cities and Their Provinces Dataset Description This dataset contains a comprehensive list of cities from Pakistan, along with their corresponding provinces. It serves as a valuable resource for anyone seeking geographical insights into Pakistan’s urban areas. The dataset covers major cities from all provinces, including Sindh, Punjab, Khyber Pakhtunkhwa, and Balochistan, making it suitable for various applications such as urban planning, population studies, and regional analysis.

Key Features:

City Names Province Names Country: Pakistan Potential Use Cases Geographical Analysis: Ideal for researchers and students performing geographical, demographic, or regional studies of Pakistan's urban landscape. Data Science Projects: Can be used for machine learning projects involving geospatial analysis, regional clustering, and city-level modeling. Visualization Projects: Helpful for creating maps, charts, and visual representations of Pakistan’s provinces and cities in tools like Power BI or Tableau. Business Insights: Useful for businesses analyzing market expansion strategies, targeting regional demographics, or performing location-based analysis. Education: A helpful resource for students and educators in geography, data science, and economics to understand the distribution of cities across provinces. Applications Machine Learning (Geospatial data, clustering models) Data Visualization (Map plotting, heatmaps) Policy Making (Urban development, resource allocation) Educational Projects (Geography, demographics) Feel free to download, explore, and incorporate this dataset into your projects. I welcome any feedback or suggestions to improve its utility!

A comprehensive dataset of 1,513 Pakistani cities, towns, tehsils, districts and places with latitude/longitude, administrative region, population (when available) and Wikidata IDs — ideal for mapping, geospatial analysis, enrichment, and location-based ML.

Why this dataset is valuable:

• Full geocoordinates for every entry (100% coverage) — ready for mapping and spatial joins.
• Wide geographic coverage across all 7 major regions of Pakistan (provinces / administrative regions).
• Wikidata IDs included for reliable cross-referencing and automatic enrichment from external knowledge bases.
• Useful for data scientists, GIS engineers, civic tech projects, academic research, and startups building Pakistan-focused location services.

Highlights (fetched from the data):

• Total rows: 1,513
• Unique places (city field): 1,497
• Rows with population > 0: 526 (≈34.8%)
• Coordinate coverage: 1513 / 1513 (100%) — directly usable with mapping libraries.

Column definitions (short):

• id — Internal numeric row id (unique integer).
• wikiDataId — Wikidata QID (e.g., Q####) for the place; use to fetch rich metadata.
• type — Administrative/place type (e.g., ADM1, ADM2, city, district, tehsil).
• city — Common/local city/place name (short label).
• name — Full name / official name of the place (may include “District”, “Tehsil”, etc.).
• country — Country name (Pakistan).
• countryCode — ISO country code (e.g., PK).
• region — Primary administrative region / province (e.g., Punjab, Sindh).
• regionCode — Short code for region (e.g., PB, KP depending on your encoding).
• regionWdId — Wikidata QID for the region.
• latitude — Latitude in decimal degrees (float).
• longitude — Longitude in decimal degrees (float).
• population — Integer population (0 or NA where unknown).

Typical & high-value use cases:

• Mapping & visualization: choropleth maps, point overlays, heatmaps of population or density.
• Geospatial analysis: distance calculations, nearest-neighbor queries, clustering of urban centers.
• Data enrichment: join with other datasets (OpenStreetMap, Wikidata, census data) using wikiDataId and coordinates.
• Machine learning & NLP: training geolocation models, geoparsing, toponym resolution, place name disambiguation.
• Urban planning & research: analyze distribution of population-ready places vs administrative units.
• Mobile / location-based apps: lookup & reverse geocoding fallback, seeding POI databases for Pakistan.
• Humanitarian & disaster response: baseline location lists for logistics and situational awareness.

Major Cities Population

This dataset provides insights into what is the population of some of the major cities in Pakistan - The dataset is sorted from highest to lowest according to the population of the cities. - This dataset also contains the population count from the census of 1998. - In which province the city is located. - Also the percentage of change in population growth from census 1998 to census 2017.

You can use this dataset in your research and analysis to gain a better understanding of Pakistani Population growth.

Note: Only major cities are included in this dataset not every city/village of Pakistan is included in this.

Graph and download economic data for Geographical Outreach: Number of Automated Teller Machines (ATMs) in 3 Largest Cities for Pakistan (PAKFCACLNUM) from 2004 to 2015 about ATM, Pakistan, banks, and depository institutions.

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Commercial Banks for Pakistan (PAKFCBODCLNUM) from 2004 to 2015 about branches, Pakistan, banks, and depository institutions.

This comprehensive dataset provides detailed population statistics for major cities across Pakistan, spanning multiple census years from 1972 to 2023. The dataset includes population figures for each city as recorded in the 1972, 1981, 1998, 2017, and 2023 censuses, along with the percentage change in population between consecutive censuses. The data is organized by city and province, offering valuable insights into urban growth trends, demographic shifts, and regional development over the past five decades.

Features

• City: Name of the city.

• Pop (2023 Census): Population as per the 2023 census, with percentage change from the 2017 census.

• Pop (2017 Census): Population as per the 2017 census, with percentage change from the 1998 census.

• Pop (1998 Census): Population as per the 1998 census, with percentage change from the 1981 census.

• Pop (1981 Census): The Population as of the 1981 census, with a percentage change from the 1972 census.

• Pop (1972 Census): Population as per the 1972 census.

• Province: The province or administrative region where the city is located.

Potential Use Cases

• Urban Planning: Analyze population growth trends to inform infrastructure development and resource allocation.

• Demographic Studies: Study the demographic changes in different regions of Pakistan over time.

• Policy Making: Support evidence-based policy decisions related to housing, education, healthcare, and transportation.

• Academic Research: Utilize the dataset for research in urban studies, sociology, and economics.

Data Source

This dataset's data was collected and compiled from the Wikipedia page titled "List of cities in Pakistan by population." The information on Wikipedia is based on publicly available census data and government records, which have been aggregated and presented in a structured format. While Wikipedia serves as a secondary source, the original data is derived from official census reports conducted by the Pakistan Bureau of Statistics and other governmental bodies.

Acknowledgments We acknowledge Wikipedia for providing a consolidated and accessible source of information on city-wise population data in Pakistan. Additionally, we extend our gratitude to the Pakistan Bureau of Statistics and other government agencies responsible for conducting and publishing the census data, which forms the foundation of this dataset. Their efforts in collecting and maintaining accurate demographic records have made this dataset possible.

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Deposit Taking Microfinance Institutions (MFIs) for Pakistan (PAKFCBODMFLNUM) from 2004 to 2015 about microfinance, branches, Pakistan, and deposits.

Digital point dataset of Major Cities of Pakistan. This dataset is Basic Vector layer derived from ESRI Map & Data 2001.

Significant sources of water pollution in Pakistan include industrial waste, agricultural runoff, sewage discharge, and waste dumping Contaminants such as heavy metals, pesticides, and untreated sewage pose a severe threat to human health and the environment Groundwater contamination is also prevalent, largely due to over-extraction and poor waste management practices Air quality:

Industrial emissions, vehicular traffic, construction activities, and the burning of solid waste cause air pollution in Pakistan High levels of particulate matter (PM), sulfur dioxide (SO2), and nitrogen dioxide (NO2) are major concerns in cities such as Lahore, Karachi, and Islamabad Air pollution affects public health, causing respiratory problems, heart disease, and stroke. The lack of proper regulation and enforcement of environmental standards exacerbates the problem. Data was initially taken from Numbeo as an aggregation of user voting.

Air quality varies from 0 (bad quality) to 100 (top good quality)

Water pollution varies from 0 (no pollution) to 100 (extreme pollution)

This dataset is one of the public parts of the City API project data. Need more? Try our full data

This horizontal bar chart displays ESG score (/ 100) by city using the aggregation average in Pakistan. The data is about companies.

This statistic shows the population living in cities in Pakistan from 2005 to 2016, arranged by city size. In 2015, there were approximately ***** million inhabitants living in cities with less than *** thousand people in Pakistan.

This is a dataset of highly populated cities of pakistan. It contains data of about top 100 cities according to population. I scraped this dataset from wikipedia.

About Dataset: There are 6 columns in this dataset and 100 rows. The column names are ranking, name, population, growth rate, and province name.

Content

The current population of Pakistan is 229,160,509 as of Wednesday, June 8, 2022, based on Worldometer elaboration of the latest United Nations data. This three datasets contain population data of Pakistan (2020 and historical), population forecast and population in major cities.

Attribute Information

• Year - Years from 2020-1955
• Population - Population in the respective year
• Yearly % Change - Percentage Yearly Change in Population
• Yearly Change - Yearly Change in Population
• Migrants (net) - Total number of migrants
• Median Age - Median age of the population
• Fertility Rate - Fertility rate
• Density (P/Km²)- Population density (population per square km)
• Urban Pop %- Percentage of urban population
• Urban Population- Urban population
• Country's Share of World Pop - Population share
• World Population - World Population in the respective year
• Pakistan Global Rank - Global Rank in Population

Source

Link : https://www.worldometers.info/world-population/pakistan-population/

Updated Covid 19 and Other Datasets

Link : https://www.kaggle.com/anandhuh/datasets

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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]

Dataset

This dataset was created by Waqas Ahmed

Contents

Abstract

The Pakistan Social and Living Standards Measurement Survey (PSLM) 2005-06 is aimed to provide detailed outcome indicators on Education, Health, Population Welfare, Water & Sanitation and Income & Expenditure. The data provided by this survey is used by the government in formulating the policies in social sector initiated under Poverty Reduction Strategy Paper (PRSP) and Medium Term Development Framework (MTDF) in the overall context of MDGs.

Geographic coverage

National Coverage

Analysis unit

Households and Individuals.

Universe

The universe of this survey consists of all urban and rural areas of the four provinces and Islamabad excluding military restricted areas

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame:

The Federal Bureau of Statistics (FBS) has developed its own urban area frame, which was up-dated in 2003. Each city/town has been divided into enumeration blocks consisting of 200- 250 households identifiable through sketch map. Each enumeration block has been classified into three categories of income groups i.e. low, middle and high keeping in view the living standard of the majority of the people. List of villages published by Population Census Organization obtained as a consequence of Population Census 1998 has been taken as rural frame.

Stratification Plan:

A. Urban Domain: Islamabad, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Bahawalpur, Sargodha, Sialkot, Karachi, Hyderabad, Sukkur, Peshawar and Quetta, have been considered as large sized cities. Each of these cities constitute a separate stratum and has further been sub-stratified according to low, middle and high-income groups. After excluding population of large sized city (s), the remaining urban population in each defunct Division in all the provinces has been grouped together to form a stratum.

B. Rural Domain: Each district in the Punjab, Sindh and NWFP provinces has been grouped together to constitute a stratum. Whereas defunct administrative Division has been treated as stratum in Balochistan province.

Sample Size and Its Allocation: Keeping in view the objectives of the survey the sample size for the four provinces has been fixed at 15453 households comprising 1109 sample village/ enumeration blocks, which is expected to produce reliable results.

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

Selection of Primary Sampling Units (PSUs): Villages and enumeration blocks in urban and rural areas respectively have been taken as Primary Sampling Units (PSUs). Sample PSUs have been selected from strata/sub-strata with PPS method of sampling technique.

Selection of Secondary Sampling Units (SSUs): Households within sample PSUs have been taken as Secondary Sampling Units (SSUs). A specified number of households i.e. 16 and 12 from each sample PSU of rural & urban area have been selected respectively using systematic sampling technique with a random start.

Mode of data collection

Face-to-face [f2f]

Research instrument

At both individual and household level, the PSLM Survey collects information on a wide range of topics using an integrated questionnaire. The questionnaire comprises a number of different sections, each of which looks at a particular aspect of household behavior or welfare. Data collected under Round II include education, diarrhea, immunization, reproductive health, pregnancy history, maternity history, family planning, pre and post-natal care and access to basic services.

Cleaning operations

Data quality in PSLM Survey has been ensured through 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 FBS headquarter Islamabad and the data entry programme used had a number of in built consistency checks.

Data appraisal

To determine the reliability of the estimates, Coefficient of Variation (CV’s) and confidence Limit of important key indicators have been worked out and are attached as Appendix - C of the survey report (provided under Related Materials).

The sample for the Multiple Indicator Cluster Survey (MICS) Punjab 2011 was designed by Pakistan Bureau of Statistics (PBS), to provide estimates on a large number of indicators on the situation of women and children including the socio-economic indicators at the provincial level for 9 divisions, 36 districts, 150 tehsils /towns, major cities, other urban and rural areas. The sample design was reviewed for adequacy and soundness by international consultants engaged by UNICEF Pakistan. The sample was selected in two stages. Within each of the 287 sampling domains, Enumeration Areas (EA) (enumeration blocks in urban areas or village/ mouzas/ dehs in rural areas) were selected with probability proportional to size. Prior to the survey implementation, a complete listing of households in all the selected EAs was conducted. Based on the total number of households in each EA a systematic sample of 12 households in urban and 16 households in rural areas was randomly drawn. This formed the second stage of sampling. In selected households, all females aged 15-49 years and children under five years were identified for individual interviews. The total sample size for the survey was 102,048 households. The sample was not self-weighting and sample weights were used to report results.