Botswana Cities with Latitude & Longitude – Download in Excel, CSV, SQL, JSON, XML
Last update : 23 March 2026.
Here you’ll find a curated sample of 100 key cities from Botswana, each with essential data points such as latitude, longitude, administrative region, and other relevant attributes.
This preview is extracted from our full dataset, which includes a total of 705 geographic locations across Botswana.
Whether you’re working on mapping, analytics, or app development, the data is available for both personal and commercial use.
All entries can be downloaded in five formats: Excel (.xlsx), CSV, SQL, JSON, and XML.
Capital Highlight: The official capital city of Botswana is Gaborone.
| Geoname_ID | City | Alternate_Name | Country_Code | Region | Sub_region | Latitude | Longitude | Elevation | Population | Timezone | Fcode_Name |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 933491 | Mabuli | BW | -25.78333 | 24.6 | 1665 | Africa/Gaborone | populated place | ||||
| 933193 | Old Tati | BW | North-East | -21.51667 | 27.8 | 0 | Africa/Gaborone | populated place | |||
| 7571024 | Pole | Pole | BW | North-East | -20.60973 | 27.58017 | 0 | Africa/Gaborone | populated place | ||
| 933655 | Kgamanes Post | BW | -19.55 | 22.25 | 0 | Africa/Gaborone | populated place | ||||
| 11288059 | Tshootsha | BW | Ghanzi | -22.14921 | 20.85638 | 0 | Africa/Gaborone | populated place | |||
| 7573801 | Senete | Senete | BW | Central | -20.32562 | 27.13016 | 0 | Africa/Gaborone | populated place | ||
| 7573790 | Mengwe | BW | North-East | -20.08916 | 27.17941 | 0 | Africa/Gaborone | populated place | |||
| 933313 | Makobeng | Makobeng,Mokobeng | BW | Central | -22.99637 | 27.66766 | 1822 | Africa/Gaborone | populated place | ||
| 933325 | Mokaya | BW | -18.91667 | 22.71667 | 0 | Africa/Gaborone | populated place | ||||
| 933719 | Janeng | Janeng | BW | South-East | -25.41667 | 25.55 | 16853 | Africa/Gaborone | populated place | ||
| 7575027 | Khalaphuduhudu | BW | Central | -21.19665 | 26.45278 | 0 | Africa/Gaborone | populated place | |||
| 932987 | Tshabong | Cabongas,Chabong,TBY,Tsabong,Tsampon’nk,Tshabong,cha bang,chabong,tshabwng bwtswana,tsuabon,Čabongas,Τσαμπόνγκ,Чабонг,טסאבונג,تشابونگ، بوتسوانا,ツァボン,察邦,차봉 | BW | Kgalagadi | -26.05 | 22.45 | 6591 | Africa/Gaborone | seat of a first-order administrative division | ||
| 933126 | Sanie | BW | -25.43333 | 22.9 | 0 | Africa/Gaborone | populated place | ||||
| 7659475 | Mmokwe | BW | Central | -22.32361 | 26.74634 | 0 | Africa/Gaborone | populated place | |||
| 7863985 | Lentsweng | BW | Kgatleng | -23.86333 | 26.29279 | 0 | Africa/Gaborone | populated place | |||
| 933622 | Kkhoutsa | BW | Ghanzi | -21.58333 | 21.56667 | 0 | Africa/Gaborone | populated place | |||
| 933091 | Serokhe | BW | Central | -23.05 | 27.36667 | 0 | Africa/Gaborone | populated place | |||
| 933521 | Lobatse | LOQ,Lobace,Lobatse,Lobatsi,Lompatse,lobache,luo ba ce,lwbats bwtswana,robatsue,Λομπάτσε,Лобаце,لوباتس، بوتسوانا,لوباٹسے,ロバツェ,洛巴策,로바체 | BW | Lobatse | -25.22435 | 25.67728 | 30883 | Africa/Gaborone | populated place | ||
| 933506 | Lotlhakane | Lotlakani,Lotlhakane | BW | Ngwaketsi | -25.08612 | 25.4586 | 0 | Africa/Gaborone | populated place | ||
| 933263 | Motloutse | Maklauts,Maklautsi,Motloutse | BW | -22.01667 | 28.41667 | 0 | Africa/Gaborone | populated place | |||
| 933878 | Boleki Village | Boleki,Boleki Village | BW | -19.91667 | 25.1 | 0 | Africa/Gaborone | populated place | |||
| 7759599 | Dzerudwana | BW | Central | -21.56057 | 26.63799 | 0 | Africa/Gaborone | populated place | |||
| 933184 | Otse | Ootsi,Ootsi Siding,Otse | BW | South-East | -25.01667 | 25.73333 | 6275 | Africa/Gaborone | populated place | ||
| 11353119 | Goshwe | BW | Central | -20.50572 | 27.20568 | 0 | Africa/Gaborone | populated place | |||
| 11204902 | Zoroga | BW | Central | -20.16472 | 25.82639 | 0 | Africa/Gaborone | populated place | |||
| 933271 | Mosopa | Moshupa,Mosopa | BW | Ngwaketsi | -24.7718 | 25.42156 | 19561 | Africa/Gaborone | populated place | ||
| 932996 | Totoru | BW | Ngwaketsi | -25.65 | 25.01667 | 0 | Africa/Gaborone | populated place | |||
| 7751775 | Tlapana | BW | Central | -21.98942 | 26.83853 | 0 | Africa/Gaborone | populated place | |||
| 933099 | Selebi-Phikwe | PKW,Phikwe,Pikwe,Pikwe-Selibe,Selebi,Selebi Pikve,Selebi Pikvė,Selebi Pikwe,Selebi-Phikwe,Selebi-Pikwe,Selebi-Pkhikve,Selempi-Fikoue,Selibe,Selibe Phikwe,Selibe-Phikwe,Selibe-Pikwe Mine Lease Area,sai lai bi-pi kui,sellebipikwe,slyb fykwh bwtswana,Σελέμπι-Φίκουε,Селеби-Пхикве,Селебі-Пхікве,سلیب فیکوه، بوتسوانا,سیلیبی-فیکوے,セレビ・ピクウェ,塞莱比-皮奎,셀레비피퀘 | BW | Selibe Phikwe | -21.97895 | 27.84296 | 42488 | Africa/Gaborone | populated place | ||
| 7751778 | Dimaje | BW | Central | -21.77229 | 26.83413 | 0 | Africa/Gaborone | populated place | |||
| 7845567 | Goomosweu | BW | Central | -22.65506 | 27.64999 | 0 | Africa/Gaborone | populated place | |||
| 7745720 | Lekgolobotlo | BW | Ngwaketsi | -24.90923 | 25.62498 | 0 | Africa/Gaborone | populated place | |||
| 933677 | Kasane | BBK,Kasane,ka sa nei,kasan bwtswana,kasane,Касане,کاسان، بوتسوانا,カサネ,卡薩內,카사네 | BW | Chobe | -17.80165 | 25.16024 | 9250 | Africa/Gaborone | seat of a first-order administrative division | ||
| 11280563 | Madisakwana | BW | Central | -21.45 | 27.47389 | 0 | Africa/Gaborone | populated place | |||
| 7883456 | Seleka | Seleka | BW | Central | -22.88077 | 27.52889 | 0 | Africa/Gaborone | populated place | ||
| 11593974 | Sowa | SXN | BW | Sowa Town | -20.564 | 26.224 | 0 | Africa/Gaborone | populated place | ||
| 933532 | Levisfontein | BW | -19.58333 | 21.15 | 0 | Africa/Gaborone | populated place | ||||
| 933643 | Khnaitso | BW | Ghanzi | -21.75 | 21.48333 | 0 | Africa/Gaborone | populated place | |||
| 7694647 | Mabolwe | Mabolwe | BW | Central | -21.82117 | 28.82004 | 0 | Africa/Gaborone | populated place | ||
| 7908686 | Lentsweletau | Lentsweletau | BW | Kweneng | -24.24863 | 25.85215 | 0 | Africa/Gaborone | populated place | ||
| 933247 | Murumush | BW | -24.08333 | 23.36667 | 0 | Africa/Gaborone | populated place | ||||
| 11427990 | Ramaphatle | BW | Kweneng | -24.6972 | 25.5962 | 0 | Africa/Gaborone | populated place | |||
| 933253 | Mswazi Village | Mswazi,Mswazi Village | BW | Central | -20.6 | 27.21667 | 0 | Africa/Gaborone | populated place | ||
| 933102 | Sekoma | Sekoma | BW | Ngwaketsi | -24.4 | 23.88333 | 1083 | Africa/Gaborone | populated place | ||
| 7686124 | Shalako | BW | Central | -22.32034 | 27.17852 | 0 | Africa/Gaborone | populated place | |||
| 7570919 | Marapong | Marapong | BW | Central | -20.89285 | 27.07206 | 0 | Africa/Gaborone | populated place | ||
| 933704 | Kgagodi | Kakhoudi,Kgagodi | BW | Central | -22.37995 | 27.56518 | 0 | Africa/Gaborone | populated place | ||
| 933216 | Njinga | BW | -18.78333 | 22.73333 | 0 | Africa/Gaborone | populated place | ||||
| 932961 | Wegdraai | BW | -25.33333 | 23.5 | 0 | Africa/Gaborone | populated place | ||||
| 7663145 | Maila | BW | Central | -22.12758 | 26.84501 | 0 | Africa/Gaborone | populated place | |||
| 7892001 | Keng | BW | Ngwaketsi | -24.59905 | 23.75522 | 0 | Africa/Gaborone | populated place | |||
| 7752616 | Mmankgodi | Mmankgodi | BW | Kweneng | -24.72601 | 25.64963 | 0 | Africa/Gaborone | populated place | ||
| 7889492 | Gasekhukhu | BW | Ngwaketsi | -24.42245 | 23.55758 | 0 | Africa/Gaborone | populated place | |||
| 7884618 | Tapalamonong | BW | Central | -23.04832 | 26.85561 | 0 | Africa/Gaborone | populated place | |||
| 7860537 | Bolawe | BW | Central | -23.54974 | 26.38613 | 0 | Africa/Gaborone | populated place | |||
| 933553 | Lefase | BW | Chobe | -18.16667 | 24.06667 | 0 | Africa/Gaborone | populated place | |||
| 11352132 | Habu | BW | North-West | -19.84 | 22.38472 | 0 | Africa/Gaborone | populated place | |||
| 933175 | Paranatungu | BW | Chobe | -18.05 | 24.26667 | 0 | Africa/Gaborone | populated place | |||
| 933533 | Letsili | BW | Central | -21.96667 | 28.51667 | 0 | Africa/Gaborone | populated place | |||
| 7756498 | Tsepe | BW | Central | -21.78436 | 26.13736 | 0 | Africa/Gaborone | populated place | |||
| 11395950 | Medie | BW | South-East | -24.57543 | 25.90441 | 0 | Africa/Gaborone | populated place | |||
| 933629 | Ki-e-Wonga | BW | Central | -20.33333 | 24.31667 | 0 | Africa/Gaborone | populated place | |||
| 932947 | Zuwe | BW | Ghanzi | -23.05 | 24.83333 | 0 | Africa/Gaborone | populated place | |||
| 933902 | Raphiri | Aphiri,Raphiri | BW | Central | -22.592 | 27.58737 | 0 | Africa/Gaborone | populated place | ||
| 7849582 | Ikongwe | Ikongwe | BW | Central | -23.13563 | 26.42269 | 0 | Africa/Gaborone | populated place | ||
| 933150 | Rakops | Rakops | BW | -21.05 | 24.41667 | 0 | Africa/Gaborone | populated place | |||
| 933888 | Boatenwana | BW | -23.61667 | 25.91667 | 0 | Africa/Gaborone | populated place | ||||
| 7653986 | Paje | Paje | BW | Central | -22.26534 | 26.79185 | 0 | Africa/Gaborone | populated place | ||
| 7859156 | Monate | BW | Central | -23.26781 | 26.90075 | 0 | Africa/Gaborone | populated place | |||
| 7663550 | Tewane | Tewane | BW | Central | -22.85335 | 26.90555 | 0 | Africa/Gaborone | populated place | ||
| 933509 | Lolale | BW | Ngwaketsi | -24.63333 | 23.51667 | 0 | Africa/Gaborone | populated place | |||
| 933813 | Detoie | BW | Central | -21.55 | 25.66667 | 0 | Africa/Gaborone | populated place | |||
| 7573760 | Dabgwi | BW | North-East | -20.22493 | 27.24986 | 0 | Africa/Gaborone | populated place | |||
| 7573818 | Tutume | Tutume,tu tu mei,twtwm bwtswana,Тутуме,توتوم، بوتسوانا,圖圖梅 | BW | Central | -20.4943 | 27.03479 | 0 | Africa/Gaborone | populated place | ||
| 933780 | Five Dunes | BW | -26.83333 | 21 | 0 | Africa/Gaborone | populated place | ||||
| 933797 | Ditsinane | BW | Central | -21.41667 | 25.83333 | 0 | Africa/Gaborone | populated place | |||
| 933084 | Sesaloa | BW | Central | -21.66667 | 25.71667 | 0 | Africa/Gaborone | populated place | |||
| 933786 | Dwaha | BW | Central | -20.93333 | 24.65 | 0 | Africa/Gaborone | populated place | |||
| 932980 | Tshwane | BW | Ghanzi | -22.4 | 22.05 | 214 | Africa/Gaborone | populated place | |||
| 7571346 | Matenge | BW | North-East | -20.85315 | 27.26287 | 0 | Africa/Gaborone | populated place | |||
| 7571030 | Ramokgwebane | BW | North-East | -20.57663 | 27.64157 | 0 | Africa/Gaborone | populated place | |||
| 933268 | Mosveng | BW | Central | -23.03333 | 27.11667 | 0 | Africa/Gaborone | populated place | |||
| 933170 | Pepascheem | Papascheem,Pepascheem | BW | -22.31667 | 25.9 | 0 | Africa/Gaborone | populated place | |||
| 7908933 | Mogobewadinonyane | BW | Ngwaketsi | -24.64902 | 24.70736 | 0 | Africa/Gaborone | populated place | |||
| 933190 | Omajaha | BW | Central | -20.36667 | 24.31667 | 0 | Africa/Gaborone | populated place | |||
| 933707 | Kai-Kai | BW | -19.88333 | 21.13333 | 0 | Africa/Gaborone | populated place | ||||
| 11258590 | Minestone | BW | City of Francistown | -21.16994 | 27.51659 | 0 | Africa/Gaborone | populated place | |||
| 7846961 | Thune Number One | Thune Number 1,Thune Number One | BW | Central | -22.09445 | 28.29494 | 0 | Africa/Gaborone | populated place | ||
| 7737589 | Mogojwagojwe | BW | Ngwaketsi | -25.29246 | 25.47551 | 0 | Africa/Gaborone | populated place | |||
| 933255 | Mpechukudu | BW | Central | -21.81667 | 25.78333 | 0 | Africa/Gaborone | populated place | |||
| 7926644 | Senyawe | BW | North-East | -20.78169 | 27.68922 | 0 | Africa/Gaborone | populated place | |||
| 7659495 | Majeadinare | BW | Central | -22.3698 | 26.54191 | 0 | Africa/Gaborone | populated place | |||
| 7765652 | Serule | BW | Central | -21.91961 | 27.29593 | 0 | Africa/Gaborone | populated place | |||
| 7745698 | Ramating | BW | Ngwaketsi | -24.82003 | 25.59752 | 0 | Africa/Gaborone | populated place | |||
| 933735 | Gwaraha | BW | Central | -20.76667 | 24.83333 | 0 | Africa/Gaborone | populated place | |||
| 11238061 | Musi | BW | Ngwaketsi | -25.64999 | 25.12743 | 0 | Africa/Gaborone | populated place | |||
| 7683657 | Majweng | BW | Central | -22.23056 | 27.35051 | 0 | Africa/Gaborone | populated place | |||
| 933885 | Bobonong | Bahonong,Bobonong,bo bo nong,bwbwnwng bwtswana,Бобононг,بوبونونگ، بوتسوانا,博博農 | BW | Central | -21.96554 | 28.43625 | 0 | Africa/Gaborone | populated place | ||
| 933005 | Tlokhoetonnas | BW | -19.51667 | 22.96667 | 0 | Africa/Gaborone | populated place | ||||
| 933021 | Manaledi | Manaledi,Tau,Tau Stadt | BW | Central | -22.68733 | 27.47713 | 0 | Africa/Gaborone | populated place |
Botswana: Mapping Resilience in the Heart of Southern Africa
A Land Defined by Space, Silence, and Subtle Shifts
Botswana is vast—over 580,000 square kilometers of predominantly semi-arid terrain—but it is not empty. From the thriving pulse of Gaborone to the dust-blown trade routes of Maun, every city and settlement in this landlocked nation tells a story of survival, innovation, and intricate adaptation to geography. As a geographer, Botswana invites both fascination and humility; it is a country where the land dictates life.
What makes Botswana unique is not just the iconic Okavango Delta or the Kalahari Desert, but the elegant balance between nature and human settlement. Cities are not scattered at random; they grow around water, mining corridors, or centuries-old migratory paths. Understanding where cities are located—and why—is impossible without granular, accurate geographic data.
Why Localized Urban Data Is Vital for Botswana
Botswana’s economy, deeply tied to diamonds and tourism, depends on infrastructure and resource management that respects both the ecology and sociopolitical structures of the country. Cities are grouped into districts, sub-districts, and wards, but these administrative units often fail to fully reflect the on-the-ground realities of regional interaction.
That’s why precise, city-level data is essential—not only to plan smarter roads or schools, but to understand how geography governs human behavior here. The spatial relationship between mining towns like Jwaneng and surrounding villages has implications for transport policy, water use, and even language distribution. Having reliable data that captures each city’s coordinates and administrative context is no longer optional—it’s foundational.
A Database That Mirrors the Country’s Complexity
Our geographic database for Botswana offers an exhaustive list of cities, villages, and towns, each matched with its proper region and administrative division. It’s been crafted not just for completeness, but for clarity—designed for everyone from academic researchers and policy planners to GIS developers and logistics managers.
Whether you're studying desertification trends or building the next pan-African delivery route, this data offers the precision and structure required to get the job done.
Excel Format: A Game-Changer for Accessibility
With the latest update, the dataset is now available in Excel (.xlsx) format, bringing an unprecedented level of accessibility. For many professionals and researchers, Excel is not just a spreadsheet—it’s a sandbox for analysis.
Now, you can filter by district, isolate longitude ranges, visualize city density, or cross-reference population and resource access—all within an Excel file that’s ready to use right out of the gate. This addition was made specifically to empower users who may not be working in SQL environments or coding platforms but still need high-quality, structured geographic data.
Other Formats for Versatility and Integration
Of course, for those who work in more specialized environments, the Botswana dataset also comes in CSV for raw handling, SQL for relational databases, JSON for web-based applications, and XML for legacy systems. But it’s the Excel version that opens the door widest, giving everyone from field workers to regional planners a direct entry point into geographic exploration.
Use Cases: From Urban Planning to Conservation
* **Transport Planning:** Analyze city-to-city distances and road connectivity within administrative zones.
* **Wildlife Management:** Map human settlements near conservation zones to anticipate potential conflicts or collaboration zones.
* **Educational Outreach:** Identify under-served towns by cross-referencing location with access to services.
* **Economic Modeling:** Track mining city growth in relation to national economic indicators.
* **Climate Research:** Use geolocation data to link urban heat islands with arid zone expansion.
Conclusion
Botswana is a country where the land speaks with quiet force, shaping where people live, how they move, and what they build. To understand this dialogue, one must move past general maps and into precise, structured data—city by city, coordinate by coordinate.
With the new Excel format now available alongside CSV, SQL, JSON, and XML, exploring the urban geography of Botswana has never been more intuitive or more powerful. Whether you're mapping future development or studying historical migration, this dataset is your key to decoding one of Africa’s most stable, yet dynamic, nations.
