Places
Places are defined geographic locations that provide the context, structure, and hierarchy for organizing, viewing, and interpreting data.
They represent real-world locations (such as
villages,cities,provinces, orcountries) and are essential for scoping information to a specific region, jurisdiction, or population.
Why Places Matter?
➡️ Places are how we “anchor” data to where it matters.
Whether you're analyzing health data, supply chain movement, population statistics, or program outcomes, places define where that data belongs or originates from.
Here are few cases where places play a part in the system and data:
✅ Scope & Filtering – They let you view data at different geographic levels (e.g.
villagevs.country).🪜Hierarchy – Places are usually organized in a parent–child structure, from smallest local areas to global regions.
🔄Interoperability – Standardized place names or codes ensure systems can share and compare location-based data accurately.
📊Analysis – Location context is crucial for decision-making (e.g., understanding
disease outbreaks,delivery routes, ordemographic trends.)
Examples
Here’s how “places” might look in a real-world hierarchy
Village / Hamlet / Community
Smallest local unit
Household surveys, community health
Nyarugusu Village, Maple Grove
Town / Municipality / City
Urban or municipal unit
City services, local programs
Toronto, Kochi
District / County / Region
Mid-level admin unit
Regional aggregation, local government
Durham Region, Kiambu County
State / Region / Territory
Major subnational division
Regional policies, resource allocation
California, Queensland
Country / Nation
Sovereign nation-state
National statistics, policy
Canada, Indonesia
Regional Bloc / Continent / Global
Multi-country grouping
Comparative analysis, global reporting
EU, Sub-Saharan Africa
🏘️Village / Hamlet / Community (Smallest local unit)
Brief Explanation
🛣️This is the smallest administrative or recognized settlement.
🔗Used for hyper-local data collection (e.g.
individual health facility reports,household surveys, orcommunity-level interventions.)💼Examples:
🏝️Nyarugusu Village – 🏴Tanzania
🌆San Juan Barangay – 🏳️Philippines
🏙️Maple Grove – 🍁Ontario, Canada
🏙️Town / Municipality / City
Brief Explanation
🏙️A populated urban or administrative unit larger than a village, often with its own government or council.
🏷️Organizes data on
city services,public health programs,urban planning, andinfrastructure.💼Examples:
🌆Toronto – 🍁Canada
🌄Lagos – 🚩Nigeria
🏙️Kochi – 🏳️India
🗺️District / County / Region
Brief Explanation
🏞️A larger administrative division that contains multiple towns or villages. 🔗Useful for aggregating data for health regions, education districts, or local government operations.
💼Examples:
🚞Durham Region – 🍁Ontario, Canada
🌄Kiambu County – 🚩Kenya
🖼️Punjab Province – 🏳️Pakistan
🗾State / Region / Territory
Brief Explanation
🗾A major subnational division, often with significant autonomy and its own governance structures.
🔗Organizes data for regional policies, economic development, and government services.
💼Examples:
🗾California – 🗽USA
🗾Queensland – 🦘Australia
🗾Bavaria – 🍺Germany
🏴Country / Nation
Brief Explanation
🚩A sovereign state recognized by international law, often the highest level of administrative authority. 🔗Used as the primary scope for national statistics, policy-making, and global reporting. 💼Examples:
🍁Canada
🗽USA
🦘Australia
🌍Regional Bloc / Continent / Global (Optional higher-level groupings)
Brief Explanation
🗺️A grouping of multiple countries or regions.
🔗Useful for multinational comparisons, trade zones, or continental health initiatives.
💼Examples:
🌍European Union (EU) – Regional
🌍Sub-Saharan Africa – Continental
🌐Global – Worldwide reporting
Working with Places
In regards to Places, here is a list of available operations that can be done:
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