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

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

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.; ;

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.

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;

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.

Major Cities Population

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

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.

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

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.

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.

Dataset

This dataset was created by Waqas Ahmed

Contents

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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Thank You

This data set has details about the vehicles that are available for purchase in some of the major cities of Pakistan.

It contains the information like car name, location, price, registration year, mileage, engine type, transmission, registration city, colour, assembly, engine power, body type and update date of the car post.

The dataset is very diversified as it contains the data of cars with various engine types i.e. petrol, diesel, hybrid, electric. All sorts of body types and colour as well.

A few questions below, which can be answered with this data to get in the head of buying/selling car market of Pakistan.

1. Which body type has the most price in ratio to their number of units?
2. How many cars are registered in other cities than their current location?
3. Which body type has the most mileage? (This could tell us which body is being used the most)
4. Areas where most cars are being sold?
5. Which car brands are being sold for lowest mileage?
6. Cars of which manufacturing year are sold the most?
7. Which engine power car is on for the selling the most?
8. Which body type of imported / local assembly are being sold the most?
9. Which transmission type of car are up for sale the most?

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).

PK：最大城市人口占城市总人口的百分比在12-01-2017达20.922%，相较于12-01-2016的20.928%有所下降。PK：最大城市人口占城市总人口的百分比数据按年更新，12-01-1960至12-01-2017期间平均值为21.610%，共58份观测结果。该数据的历史最高值出现于12-01-1980，达23.038%，而历史最低值则出现于12-01-1960，为18.670%。CEIC提供的PK：最大城市人口占城市总人口的百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的巴基斯坦 – 表 PK.世行.WDI：人口和城市化进程统计。

This dataset provides a comprehensive overview of various hunger-related metrics in Pakistan from 2020 to 2023. It includes critical indicators such as the percentage of the population living under poverty, malnutrition rates, food insecurity levels, access to clean water, food production index, and the rate of underweight children. These metrics are essential for understanding the current state of hunger and nutritional challenges faced by the population in Pakistan.

Key Features:

Year:The year of data collection (2020-2023).

Population_Under_Poverty: The percentage of the population living below the poverty line.

Malnutrition_Rate:The percentage of the population suffering from malnutrition.

Food_Insecurity: The percentage of the population experiencing food insecurity.

Access_to_Clean_Water: The percentage of the population with access to clean water.

Food_Production_Index: An index value representing the level of food production.

Children_Underweight:The percentage of children underweight for their age.

Use Cases: This dataset is useful for analyzing trends in hunger and nutrition over recent years in Pakistan. It can support research in areas such as public health, economic development, and food security. The data is valuable for policymakers, researchers, and organizations focused on addressing hunger and improving nutritional outcomes.