Major Cities in Pakistan by Population

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.

Description: This dataset contains detailed information about cities and administrative regions in Pakistan, including their geographic coordinates, population, and administrative hierarchy. Each record provides:

id – Unique identifier for the location.

wikiDataId – Wikidata entity ID for cross-referencing.

type – Classification of the place (CITY, ADM2).

city – City or locality name.

name – Full official name of the place.

country – Country name (Pakistan).

countryCode – ISO 2-letter country code.

region – Province or state name.

regionCode – Abbreviation for the province.

regionWdId – Wikidata ID for the province/region.

latitude – Latitude coordinate.

longitude – Longitude coordinate.

population – Population count (0 means unknown or unavailable).

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.

This dataset is about cities in Pakistan. It has 349 rows. It features 4 columns: country, population, and region.

Pakistan PK: Population in Largest City data was reported at 15,020,931.000 Person in 2017. This records an increase from the previous number of 14,650,981.000 Person for 2016. Pakistan PK: Population in Largest City data is updated yearly, averaging 6,793,799.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 15,020,931.000 Person in 2017 and a record low of 1,853,325.000 Person in 1960. Pakistan PK: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Actual value and historical data chart for Pakistan Population In Largest City

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.

Pakistan PK: Population in Largest City: as % of Urban Population data was reported at 20.922 % in 2017. This records a decrease from the previous number of 20.928 % for 2016. Pakistan PK: Population in Largest City: as % of Urban Population data is updated yearly, averaging 21.610 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 23.038 % in 1980 and a record low of 18.670 % in 1960. Pakistan PK: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Context

Simple Pakistani Cities Data, with latitude, longitude, population, Province. Below is a list of 146 prominent cities in Pakistan. Each row includes a city's latitude, longitude, province and other variables of interest. This is a subset of all 140,909 places in Pakistan (and only some of the fields) that you'll find in our World Cities Database.

Source

https://simplemaps.com/data/pk-cities

Major Cities Population

About this dataset

Context

This list ranks the 1,430 cities in the New York by Pakistani population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Pakistani Population: This column displays the rank of city in the New York by their Pakistani population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Pakistani Population: The Pakistani population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Pakistani. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total New York Pakistani Population: This tells us how much of the entire New York Pakistani population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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!

About this dataset

Context

This list ranks the 238 cities in the Washington by Pakistani population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Pakistani Population: This column displays the rank of city in the Washington by their Pakistani population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Pakistani Population: The Pakistani population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Pakistani. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Washington Pakistani Population: This tells us how much of the entire Washington Pakistani population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

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.

Dataset

This dataset was created by Jabran Noor

Contents

The share of urban population in Pakistan amounted to 38.04 percent in 2023. In a steady upward trend, the share rose by 15.94 percentage points from 1960.

Abstract

The Pakistan Integrated Household Survey (PIHS) was conducted jointly by the Federal Bureau of Statistics (FBS), Government of Pakistan, and the World Bank. The survey was part of the Living Standards Measurement Study (LSMS) household surveys that have been conducted in a number of developing countries with the assistance of the World Bank. The purpose of these surveys is to provide policy makers and researchers with individual, household, and community level data needed to analyze the impact of policy initiatives on living standards of households.

The Pakistan Integrated Household Survey was carried out in 1991. This nationwide survey gathered individual and household level data using a multi-purpose household questionnaire. Topics covered included housing conditions, education, health, employment characteristics, selfemployment activities, consumption, migration, fertility, credit and savings, and household energy consumption. Community level and price data were also collected during the course of the survey.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the PIHS was drawn using a multi-stage stratified sampling procedure from the Master Sample Frame developed by FBS based on the 1981 Population Census.

SAMPLE FRAME:

This sample frame covers all four provinces (Punjab, Sindh, NWFP, and Balochistan) and both urban and rural areas. Excluded, however, are the Federally Administered Tribal Areas, military restricted areas, the districts of Kohistan, Chitral and Malakand and protected areas of NWFP. According to the FBS, the population of the excluded areas amounts to about 4 percent of the total population of Pakistan. Also excluded are households which depend entirely on charity for their living.

The sample frame consists of three main domains: (a) the self-representing cities; (b) other urban areas; and (c) rural areas. These domains are further split up into a number of smaller strata based on the system used by the Government to divide the country into administrative units. The four provinces of Pakistan mentioned above are divided into 20 divisions altogether; each of these divisions in turn is then further split into several districts. The system used to divide the sample frame into the three domains and the various strata is as follows: (a) Self-representing cities: All cities with a population of 500,000 or more are classified as self-representing cities. These include Karachi, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Hyderabad and Peshawar. In addition to these cities, Islamabad and Quetta are also included in this group as a result of being the national and provincial capitals respectively. Each self-representing city is considered as a separate stratum, and is further sub-stratified into low, medium, and high income groups on the basis of information collected at the time of demarcation or updating of the urban area sample frame. (b) Other urban areas: All settlements with a population of 5,000 or more at the time of the 1981 Population Census are included in this group (excluding the self-representing cities mentioned above). Urban areas in each division of the four provinces are considered to be separate strata. (c) Rural areas: Villages and communities with population less than 5,000 (at the time of the Census) are classified as rural areas. Settlements within each district of the country are considered to be separate strata with the exception of Balochistan province where, as a result of the relatively sparse population of the districts, each division instead is taken to be a stratum.

Main strata of the Master Sample frame

Domain / Punjab / Sindh / NWFP / Balochistan / PAKISTAN Self-representing cities / 6 / 2 / 1 / 1 / 10 Other urban areas / 8 / 3 / 5 / 4 / 20 Rural areas / 30 / 14 / 10 / 4 / 58 Total 44 / 19 / 16 / 9 / 88

As the above table shows, the sample frame consists of 88 strata altogether. Households in each stratum of the sample frame are exclusively and exhaustively divided into PSUs. In urban areas, each city or town is divided into a number of enumeration blocks with welldefined boundaries and maps. Each enumeration block consists of about 200-250 households, and is taken to be a separate PSU. The list of enumeration blocks is updated every five years or so, with the list used for the PIHS having been modified on the basis of the Census of Establishments conducted in 1988. In rural areas, demarcation of PSUs has been done on the basis of the list of villages/mouzas/dehs published by the Population Census Organization based on the 1981 Census. Each of these villages/mouzas/dehs is taken to be a separate PSU. Altogether, the sample frame consists of approximately 18,000 urban and 43,000 rural PSUs.

SAMPLE SELECTION:

The PIHS sample comprised 4,800 households drawn from 300 PSUs throughout the country. Sample PSUs were divided equally between urban and rural areas, with at least two PSUs selected from each of the strata. Selection of PSUs from within each stratum was carried out using the probability proportional to estimated size method. In urban areas, estimates of the size of PSUs were based on the household count as found during the 1988 Census of Establishments. In rural areas, these estimates were based on the population count during the 1981 Census.

Once sample PSUs had been identified, a listing of all households residing in the PSU was made in all those PSUs where such a listing exercise had not been undertaken recently. Using systematic sampling with a random start, a short-list of 24 households was prepared for each PSU. Sixteen households from this list were selected to be interviewed from the PSU; every third household on the list was designated as a replacement household to be interviewed only if it was not possible to interview either of the two households immediately preceding it on the list.

As a result of replacing households that could not be interviewed because of non-responses, temporary absence, and other such reasons, the actual number of households interviewed during the survey - 4,794 - was very close to the planned sample size of 4,800 households. Moreover, following a pre-determined procedure for replacing households had the added advantage of minimizing any biases that may otherwise have arisen had field teams been allowed more discretion in choosing substitute households.

SAMPLE DESIGN EFFECTS:

The three-stage stratified sampling procedure outlined above has several advantages from the point of view of survey organization and implementation. Using this procedure ensures that all regions or strata deemed important are represented in the sample drawn for the survey. Picking clusters of households or PSUs in the various strata rather than directly drawing households randomly from throughout the country greatly reduces travel time and cost. Finally, selecting a fixed number of households in each PSU makes it easier to distribute the workload evenly amongst field teams. However, in using this procedure to select the sample for the survey, two important matters need to be given consideration: (a) sampling weights or raising factors have to be first calculated to get national estimates from the survey data; and (b) the standard errors for estimates obtained from the data need to be adjusted to take account for the use of this procedure.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PIHS used three questionnaires: a household questionnaire, a community questionnaire, and a price questionnaire.

HOUSEHOLD QUESTIONNAIRE:

The PIHS questionnaire comprised 17 sections, each of which covered a separate aspect of household activity. The various sections of the household questionnaire were as follows: 1. HOUSEHOLD INFORMATION 2. HOUSING 3. EDUCATION 4. HEALTH 5. WAGE EMPLOYMENT 6. FAMILY LABOR 7. ENERGY 8. MIGRATION 9. FARMING AND LIVESTOCK 10. NON-FARM ENTERPRISE ACTIVITIES 11. NON-FOOD EXPENDITURES AND INVENTORY OF DURABLE GOODS 12. FOOD EXPENSES AND HOME PRODUCTION 13. MARRIAGE AND MATERNITY HISTORY 14. ANTHROPOMETRICS 15. CREDIT AND SAVINGS 16. TRANSFERS AND REMITTANCES 17. OTHER INCOME

The household questionnaire was designed to be administered in two visits to each sample household. Apart from avoiding the problem of interviewing household members in one long stretch, scheduling two visits also allowed the teams to improve the quality of the data collected.

During the first visit to the household (Round 1), the enumerators covered sections 1 to 8, and fixed a date with the designated respondents of the household for the second visit. During the second visit (Round 2), which was normally held two weeks after the first visit, the enumerators covered the remaining portion of the questionnaire and resolved any omissions or inconsistencies that were detected during data entry of information from the first part of the survey.

Since many of the sections of the questionnaire pertained specifically to female members of the household, female interviewers were included in conducting the survey. The household questionnaire was split into two parts (Male and Female). Sections such as SECTION 3: EDUCATION, which solicited information on all individual members of the household (male as well as female) were included in both parts of the questionnaire. Other sections such as SECTION 2: HOUSING and SECTION 12: FOOD EXPENSES AND HOME PRODUCTION , which collected data at the aggregate household level, were included in either the male questionnaire or the female questionnaire, depending upon which member of the household was more likely

Dataset

This dataset was created by Asim Zahid

Released under CC0: Public Domain

Contents

Historical dataset of population level and growth rate for the Lahore, Pakistan metro area from 1950 to 2025.