Zambia cities list with latitude and longitude in Excel, CSV, XML, SQL, JSON formats
Last update : 15 February 2026.
Below is a list of 100 prominent cities in Zambia. Each row includes a city's latitude, longitude, region and other variables of interest. This is a subset of all 12745 places in Zambia that you'll find in our World Cities Database. You're free to use the data below for personal or commercial applications. The data below can be downloaded in Excel (.xlsx), .csv, .json, .xml and .sql formats. Notable Cities: The capital of Zambia is Lusaka.
| Geoname_ID | City | Alternate_Name | Country_Code | Region | Sub_region | Latitude | Longitude | Elevation | Population | Timezone | Fcode_Name |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 906187 | Mlipa | ZM | Eastern | -13.86951 | 31.91484 | 0 | Africa/Lusaka | populated place | |||
| 908568 | Mafuta | ZM | Eastern | -13.55 | 32.41667 | 0 | Africa/Lusaka | populated place | |||
| 905693 | Mshawa | ZM | Eastern | -13.51667 | 32.68333 | 0 | Africa/Lusaka | populated place | |||
| 896219 | Wanchikulo | ZM | Central | -14.68333 | 25.6 | 0 | Africa/Lusaka | populated place | |||
| 921569 | Amwalana | ZM | Western | -15.85 | 23.93333 | 0 | Africa/Lusaka | populated place | |||
| 917017 | Gunzwe | ZM | Southern | -17.53333 | 25.61667 | 0 | Africa/Lusaka | populated place | |||
| 913102 | Kapichida | ZM | Muchinga | -11.56667 | 32.91667 | 0 | Africa/Lusaka | populated place | |||
| 921000 | Chagambo | Chagamba,Chagambo | ZM | Eastern | -13.45225 | 32.87964 | 0 | Africa/Lusaka | populated place | ||
| 898732 | Shati | ZM | Western | -14.45 | 23.8 | 0 | Africa/Lusaka | populated place | |||
| 920709 | Changa | ZM | Southern | -16.28436 | 28.4021 | 0 | Africa/Lusaka | populated place | |||
| 912573 | Kasero | ZM | Eastern | -14.52 | 31.17388 | 0 | Africa/Lusaka | populated place | |||
| 911088 | Kombi | ZM | Copperbelt | -13.45743 | 28.6736 | 0 | Africa/Lusaka | populated place | |||
| 899204 | Samunyumbwe | ZM | Western | -15.55 | 24.73333 | 0 | Africa/Lusaka | populated place | |||
| 175338 | Pangala | ZM | Muchinga | -9.85 | 33.26667 | 0 | Africa/Lusaka | populated place | |||
| 898810 | Shamapula | Chamapula,Shamapula | ZM | Southern | -16.02466 | 26.05238 | 0 | Africa/Lusaka | populated place | ||
| 901776 | Namutoya | ZM | Central | -12.72738 | 30.409 | 0 | Africa/Lusaka | populated place | |||
| 916041 | Kachanga | ZM | Eastern | -14.3 | 31.31667 | 0 | Africa/Lusaka | populated place | |||
| 910648 | Lianyunga | ZM | Western | -16.2 | 22.41667 | 0 | Africa/Lusaka | populated place | |||
| 915941 | Kadange | ZM | Eastern | -12.56667 | 33.3 | 0 | Africa/Lusaka | populated place | |||
| 898633 | Shilangwe | ZM | Central | -15.27888 | 26.945 | 0 | Africa/Lusaka | populated place | |||
| 897577 | Simoni Chisenga | ZM | Central | -13.01582 | 30.91624 | 0 | Africa/Lusaka | populated place | |||
| 915198 | Kalimba | ZM | Copperbelt | -13.19356 | 28.63318 | 0 | Africa/Lusaka | populated place | |||
| 898472 | Shonena | ZM | Western | -14.71667 | 24.85 | 0 | Africa/Lusaka | populated place | |||
| 906003 | Moshoba | ZM | Lusaka | -15.35 | 28.1 | 0 | Africa/Lusaka | populated place | |||
| 916303 | Kabibi | ZM | Eastern | -13.36667 | 32.9 | 0 | Africa/Lusaka | populated place | |||
| 904531 | Mulundu | ZM | North-Western | -11.01667 | 24.11667 | 0 | Africa/Lusaka | populated place | |||
| 177368 | Chole | ZM | Northern | -8.93333 | 31.65 | 0 | Africa/Lusaka | populated place | |||
| 898137 | Sibusa | ZM | Western | -15.45 | 22.6 | 0 | Africa/Lusaka | populated place | |||
| 896223 | Wamwanu | ZM | Western | -15.4 | 23.38333 | 0 | Africa/Lusaka | populated place | |||
| 906835 | Mcekeni | ZM | Eastern | -14.13333 | 31.2 | 0 | Africa/Lusaka | populated place | |||
| 913258 | Kapampa | ZM | Muchinga | -12.35 | 32.05 | 0 | Africa/Lusaka | populated place | |||
| 913641 | Kanona | ZM | Western | -14.88333 | 24.08333 | 0 | Africa/Lusaka | populated place | |||
| 901929 | Namebo | ZM | Western | -16.23333 | 22.36667 | 0 | Africa/Lusaka | populated place | |||
| 903030 | Mwanasitutu | ZM | Western | -14.9 | 22.31667 | 0 | Africa/Lusaka | populated place | |||
| 911131 | Kokana | ZM | Western | -14.7 | 25.21667 | 0 | Africa/Lusaka | populated place | |||
| 915179 | Kalimunda | ZM | Eastern | -12.91667 | 31.96667 | 0 | Africa/Lusaka | populated place | |||
| 904049 | Muruma | ZM | Western | -17.03333 | 25.16667 | 0 | Africa/Lusaka | populated place | |||
| 914238 | Kamphasa | ZM | Eastern | -13.57363 | 32.489 | 0 | Africa/Lusaka | populated place | |||
| 899410 | Salili Kapika | ZM | Central | -12.96223 | 30.68494 | 0 | Africa/Lusaka | populated place | |||
| 918647 | Chipetuka | ZM | Eastern | -12.37901 | 33.21493 | 0 | Africa/Lusaka | populated place | |||
| 900977 | Nguvulu | ZM | North-Western | -13.4356 | 23.04311 | 0 | Africa/Lusaka | populated place | |||
| 918219 | Chitala | ZM | Central | -14.26667 | 29.86667 | 0 | Africa/Lusaka | populated place | |||
| 899242 | Samubi | ZM | Western | -14.75 | 23.83333 | 0 | Africa/Lusaka | populated place | |||
| 175242 | Semu | ZM | Northern | -9.28333 | 31.56667 | 0 | Africa/Lusaka | populated place | |||
| 911582 | Kawanga | ZM | Western | -14.46667 | 23.7 | 0 | Africa/Lusaka | populated place | |||
| 900403 | Nuala | ZM | Copperbelt | -13.41667 | 28.25 | 0 | Africa/Lusaka | populated place | |||
| 916697 | Isake Chimbwi | ZM | Central | -12.91783 | 30.39462 | 0 | Africa/Lusaka | populated place | |||
| 914908 | Kalundumuna | ZM | Western | -14.95 | 23.23333 | 0 | Africa/Lusaka | populated place | |||
| 916484 | Joseni | ZM | Eastern | -12.31667 | 33.26667 | 0 | Africa/Lusaka | populated place | |||
| 897325 | Sioma | Sioma | ZM | Western | -16.66667 | 23.53333 | 0 | Africa/Lusaka | populated place | ||
| 910716 | Lesa | Lesa,Leza | ZM | Copperbelt | -13.5 | 28.16667 | 0 | Africa/Lusaka | populated place | ||
| 905423 | Mufaka | ZM | Southern | -17.66667 | 25.56667 | 0 | Africa/Lusaka | populated place | |||
| 910430 | Lilayi | ZM | Lusaka | -15.51877 | 28.30136 | 0 | Africa/Lusaka | populated place | |||
| 910722 | Lengwa | ZM | Central | -14.78333 | 27.88333 | 0 | Africa/Lusaka | populated place | |||
| 919318 | Chimbangu | ZM | Muchinga | -11.8 | 31.66667 | 0 | Africa/Lusaka | populated place | |||
| 908778 | Lwembe | Luembi,Lwembe,Ruemba | ZM | Central | -15.16667 | 28.1 | 0 | Africa/Lusaka | populated place | ||
| 909975 | Lowe | ZM | Eastern | -13.7 | 31.61667 | 0 | Africa/Lusaka | populated place | |||
| 913987 | Kanchule Chisenga | ZM | Central | -12.77019 | 30.42908 | 0 | Africa/Lusaka | populated place | |||
| 902638 | Mwindamoyo | ZM | Western | -16.1 | 22.98333 | 0 | Africa/Lusaka | populated place | |||
| 903388 | Muyaluka | ZM | Western | -15.7 | 23.08333 | 0 | Africa/Lusaka | populated place | |||
| 900505 | Nsungu | ZM | Muchinga | -10.56667 | 32.26667 | 0 | Africa/Lusaka | populated place | |||
| 918500 | Chironga | ZM | Eastern | -13.8943 | 31.55881 | 0 | Africa/Lusaka | populated place | |||
| 920415 | Chembe | Chembe | ZM | Luapula | -11.96201 | 28.74313 | 0 | Africa/Lusaka | populated place | ||
| 900025 | Nyundo | ZM | Eastern | -14.41667 | 31.83333 | 0 | Africa/Lusaka | populated place | |||
| 901443 | Nchindo | ZM | Western | -17.48333 | 24.88333 | 0 | Africa/Lusaka | populated place | |||
| 896584 | Tsinsamba | ZM | North-Western | -11.53333 | 24.6 | 0 | Africa/Lusaka | populated place | |||
| 915871 | Kafukanya | ZM | Copperbelt | -13.2421 | 28.43898 | 0 | Africa/Lusaka | populated place | |||
| 904314 | Mungala | ZM | Western | -15.05 | 22.7 | 0 | Africa/Lusaka | populated place | |||
| 899148 | Sandemba | ZM | North-Western | -13.65 | 23.11667 | 0 | Africa/Lusaka | populated place | |||
| 904621 | Mulonda Siwila | ZM | Muchinga | -10.18333 | 32.6 | 0 | Africa/Lusaka | populated place | |||
| 910055 | Loanja | Loanja,Luanja | ZM | Western | -17.32615 | 24.78422 | 0 | Africa/Lusaka | populated place | ||
| 897536 | Simupingula | ZM | Southern | -16.75 | 26.13333 | 0 | Africa/Lusaka | populated place | |||
| 917913 | Chiwashia | ZM | Muchinga | -10.48333 | 32.11667 | 0 | Africa/Lusaka | populated place | |||
| 903709 | Muswema | Muswema,Muswemas | ZM | Muchinga | -10.03475 | 32.63862 | 0 | Africa/Lusaka | populated place | ||
| 176947 | Kambwali | ZM | Luapula | -9.41681 | 28.73779 | 0 | Africa/Lusaka | populated place | |||
| 915999 | Kachilika | ZM | Muchinga | -10.08333 | 32.51667 | 0 | Africa/Lusaka | populated place | |||
| 897173 | Situla | ZM | Western | -15.95 | 23.26667 | 0 | Africa/Lusaka | populated place | |||
| 907151 | Mayembo | Mayembe,Mayembo | ZM | Muchinga | -10.05079 | 32.52029 | 0 | Africa/Lusaka | populated place | ||
| 910301 | Liokwe | ZM | Western | -15.61667 | 23.13333 | 0 | Africa/Lusaka | populated place | |||
| 910244 | Lisamba | ZM | North-Western | -13.8 | 22.86667 | 0 | Africa/Lusaka | populated place | |||
| 902285 | Nakosa | ZM | Central | -13.38333 | 30.36667 | 0 | Africa/Lusaka | populated place | |||
| 899977 | Old Manyinga | Manyinga,Old Manyinga | ZM | North-Western | -13.30115 | 24.21979 | 0 | Africa/Lusaka | populated place | ||
| 899639 | Rusangu | ZM | Southern | -16.43333 | 27.53333 | 0 | Africa/Lusaka | populated place | |||
| 897634 | Simbeleko | ZM | Southern | -16.59727 | 26.21518 | 0 | Africa/Lusaka | populated place | |||
| 902893 | Mwape Mbusha | ZM | Central | -12.83108 | 30.28789 | 0 | Africa/Lusaka | populated place | |||
| 913177 | Kapelabulungu | ZM | Western | -17.06667 | 24.66667 | 0 | Africa/Lusaka | populated place | |||
| 920814 | Chamboka | ZM | Central | -14.69858 | 27.8829 | 0 | Africa/Lusaka | populated place | |||
| 175318 | Penza | ZM | Northern | -8.94025 | 31.54793 | 0 | Africa/Lusaka | populated place | |||
| 898199 | Siasikabole | Siasikabole | ZM | Southern | -17.10637 | 27.03753 | 0 | Africa/Lusaka | populated place | ||
| 176491 | Koko | ZM | Northern | -9.05 | 30.76667 | 0 | Africa/Lusaka | populated place | |||
| 897289 | Sirimao | ZM | Eastern | -14.08333 | 31.3 | 0 | Africa/Lusaka | populated place | |||
| 175648 | Mwatipa | ZM | Luapula | -8.55 | 29.11667 | 0 | Africa/Lusaka | populated place | |||
| 904095 | Mupasha | ZM | North-Western | -13.98333 | 23.48333 | 0 | Africa/Lusaka | populated place | |||
| 909841 | Luatembo | ZM | Western | -15.21672 | 23.79696 | 0 | Africa/Lusaka | populated place | |||
| 913920 | Kanego | ZM | Southern | -16.06667 | 26.25 | 0 | Africa/Lusaka | populated place | |||
| 915579 | Kakubuka | Kakubuka,Kakuuka | ZM | Central | -14.85 | 27.81667 | 0 | Africa/Lusaka | populated place | ||
| 898286 | Siamupila | ZM | Southern | -16.23333 | 28.15 | 0 | Africa/Lusaka | populated place | |||
| 909769 | Lubinda | ZM | Western | -15.58333 | 23.13333 | 0 | Africa/Lusaka | populated place | |||
| 920118 | Chicawnaconta | ZM | Central | -15.2 | 27.93333 | 0 | Africa/Lusaka | populated place | |||
| 919900 | Chikenge | Chikenge,Chikenze | ZM | North-Western | -13.48333 | 22.45 | 0 | Africa/Lusaka | populated place |
**Exploring Zambia: Insights from a Geographer**
Introduction**
Nestled in the heart of southern Africa, Zambia is a landlocked country brimming with natural beauty, cultural diversity, and economic potential. As a geographer delving into the intricate landscapes of this fascinating nation, our quest is to obtain data on its cities, regions, and departments, while also capturing the latitude and longitude coordinates of each urban center.
Unveiling Geographic Diversity**
Zambia's geography is characterized by a diverse range of landscapes, from the lush valleys of the Zambezi River to the vast plains of the Copperbelt region. Its cities, including the capital Lusaka, the industrial hub of Kitwe, and the tourist hotspot Livingstone, are nodes of urban activity amidst the country's natural splendor. By unraveling the regions and departments that constitute Zambia, we gain insight into its administrative divisions and spatial organization.
Tracing Spatial Coordinates**
Our journey takes us beyond mere cartography as we trace the latitude and longitude coordinates of each city across Zambia. From the bustling streets of Ndola to the tranquil shores of Lake Kariba, these geographic markers serve as navigational tools, guiding us through the diverse tapestry of urban and rural environments that define this nation.
Interpreting Spatial Patterns**
The data we gather from our geographic exploration offers valuable insights into Zambia's spatial patterns and dynamics. By analyzing the distribution of cities and regions, we can discern patterns of population density, economic activity, and infrastructure development. Moreover, we explore how geographical features such as rivers, mountains, and forests shape the spatial distribution of human settlements and land use practices.
Challenges and Opportunities**
Zambia faces a myriad of challenges, including poverty, environmental degradation, and healthcare disparities. However, amidst these challenges lie opportunities for sustainable development, conservation, and social progress. By harnessing its abundant natural resources, promoting rural development, and investing in education and healthcare, Zambia can unlock its full potential and pave the way for a brighter future for its citizens.
Conclusion**
In conclusion, our exploration of Zambia's geography has provided valuable insights into its spatial dynamics and challenges. By obtaining and analyzing data on its cities, regions, and geographic coordinates, we gain a deeper understanding of its diverse landscapes and the factors that shape them. As we continue to study and engage with Zambia, we are reminded of the importance of holistic approaches and cross-disciplinary collaboration in addressing its complex needs and fostering sustainable development.

Download data files for Zambia's cities in Excel (.xlsx), CSV, SQL, XML and JSON formats
Exploring Zambia: A Geographer’s Approach to City-Level Data
Zambia, a landlocked country in southern Africa, is known for its vast landscapes, including expansive savannas, diverse ecosystems, and vibrant urban centers. As one of the most geographically diverse countries in the region, Zambia offers a unique opportunity for geographical study. From the bustling capital of Lusaka to the smaller towns scattered across the country, understanding Zambia's cities, their regions, and administrative divisions is crucial for a deeper comprehension of its urban planning, infrastructure needs, and natural resource management. This article will delve into the value of obtaining detailed city-level data in Zambia, including latitude and longitude coordinates, as well as regional and departmental information. Additionally, it will explore how this data can be accessed in multiple formats such as CSV, SQL, JSON, and XML, providing flexibility for various research and development projects.
The Geography of Zambia: A Diverse Landscape
Zambia is bordered by eight countries—Tanzania to the northeast, Malawi to the east, Mozambique to the southeast, Zimbabwe to the south, and Angola to the west. The country spans a variety of geographic regions, from highlands and plateaus to river valleys and tropical forests. The Zambezi River, one of Africa’s most famous rivers, flows through the southern part of Zambia, and the country is also home to iconic natural landmarks like Victoria Falls, one of the Seven Natural Wonders of the World.
Lusaka, the capital and largest city, lies on the central plateau and serves as the country’s political, economic, and cultural hub. Other key cities such as Ndola, Kitwe, and Livingstone are strategically located near Zambia’s rich mineral deposits, agricultural zones, and tourism destinations. Understanding the geographic distribution of Zambia’s cities and towns, as well as the regions and departments they belong to, is essential for mapping the nation’s growth patterns, infrastructure needs, and resource management strategies.
The Importance of City-Level Geographic Data in Zambia
City-level geographic data is fundamental to understanding Zambia's urban development, regional disparities, and resource allocation. With a population spread across both urban and rural areas, Zambia faces unique challenges in managing infrastructure, public services, and environmental sustainability. By obtaining detailed data on the country’s cities—such as Lusaka, Kitwe, and the rural townships—geographers and urban planners can better address these challenges and identify areas of opportunity for growth.
City-level data, including the latitude and longitude of each city, allows for the creation of accurate maps that can be used to analyze transportation networks, water distribution systems, healthcare facilities, and education infrastructure. These maps help in identifying areas where resources may be lacking and where future investments are needed. Moreover, knowing the geographic boundaries of regions and departments provides clarity on governance structures and administrative responsibilities, which is key for effective policymaking and development.
Additionally, the importance of geographic data extends to environmental management. Zambia’s ecosystems are under pressure due to urbanization, deforestation, and climate change. Having access to detailed geographic data enables the monitoring of land use changes and provides insights into how urban expansion impacts natural resources, including forests, water bodies, and wildlife habitats.
Accessing City-Level Data for Zambia
For geographers, urban planners, and researchers, obtaining detailed city-level data for Zambia is essential for any project related to urbanization, infrastructure, or resource management. This data typically includes the geographical coordinates (latitude and longitude) of each city, as well as information about the surrounding regions and departments. By having access to such data, users can conduct in-depth spatial analyses, create detailed maps, and monitor the progress of various development projects.
The ability to access this data in formats such as CSV, SQL, JSON, and XML adds a layer of flexibility for different types of analysis. These formats make it easy to import and export data into various geographic systems, databases, or applications, streamlining the research process and supporting decision-making.
Formats for Geographic Data: CSV, SQL, JSON, and XML
Each of the four data formats—CSV, SQL, JSON, and XML—offers unique benefits, depending on the scale of the project and the user’s specific needs.
- **CSV (Comma Separated Values):** CSV is one of the simplest and most commonly used formats for geographic data. It is especially useful for working with smaller datasets or for importing data into spreadsheet programs like Excel or Google Sheets. With CSV, city-level data such as city names, latitude/longitude coordinates, population, and administrative regions can be quickly organized and analyzed.
- **SQL (Structured Query Language):** SQL is ideal for managing large datasets stored in relational databases. This format allows users to run complex queries on geographic data, making it especially beneficial for large-scale projects that require sorting, filtering, and statistical analysis. SQL is perfect for researchers and urban planners who need to handle extensive city-level data and work with multiple sources of information.
- **JSON (JavaScript Object Notation):** JSON is widely used for data exchange in web applications and APIs. It is a flexible, lightweight format that is perfect for developers who need to integrate geographic data into location-based services or mobile applications. JSON’s hierarchical structure allows for easy representation of complex data relationships, making it ideal for dynamic geographic systems.
- **XML (eXtensible Markup Language):** XML is a structured format used for exchanging data between systems. It is particularly useful for representing hierarchical data and is often used in geospatial systems to manage geographic data in a standardized format. XML is commonly used when sharing large datasets between platforms or for storing detailed geographic information.
Each of these formats makes it easier to access, manage, and analyze geographic data from Zambia, allowing for seamless integration with various research, urban planning, and policy development tools.
Why Geographic Data is Crucial for Understanding Zambia
Accurate geographic data is essential for understanding the regional and urban dynamics of Zambia. The country’s growth, particularly in cities like Lusaka, Ndola, and Kitwe, presents both opportunities and challenges. As urban areas continue to expand, managing infrastructure, services, and resources becomes increasingly complex.
By analyzing city-level geographic data, urban planners can better understand the spatial relationships between cities, rural areas, and natural resources. This information allows for informed decision-making regarding transportation, water supply, energy, and waste management. Furthermore, detailed geographic data aids in assessing land use patterns and their impact on ecosystems, helping to design sustainable development strategies.
Additionally, as Zambia is rich in natural resources such as copper and other minerals, geographic data helps to manage and optimize the extraction and distribution of these resources, while ensuring that they are used in an environmentally sustainable way.
Leveraging Geographic Data for Development Projects
The availability of city-level geographic data allows for more effective development planning, environmental conservation, and infrastructure management. By knowing where cities are located and how they are spread across Zambia’s regions, policymakers can target specific areas for investment and development.
Whether working on an infrastructure project, conducting environmental research, or developing a GIS application, the ability to access data in formats like CSV, SQL, JSON, and XML gives users the tools to integrate, analyze, and visualize geographic information. This flexibility is crucial for a wide range of applications, from urban development and resource management to environmental monitoring and disaster response.
Conclusion
Zambia’s geographic data, including detailed information about its cities, regions, and departments, is a crucial tool for understanding the country’s development patterns, urban growth, and resource management needs. By obtaining accurate latitude and longitude coordinates and regional data, researchers, urban planners, and policymakers can make informed decisions that will help address the challenges of urbanization, infrastructure development, and environmental sustainability. The availability of this data in formats such as CSV, SQL, JSON, and XML ensures that it can be easily integrated into a variety of systems and projects, providing the foundation for more effective and efficient planning in Zambia’s cities and regions.