WBG's Guideline on Geospatial Open Data Collection

Draft v20250616
BI, CI, CMH

1. Introduction

Spatial open data is a powerful resource for understanding and improving the environments in which people live and work. It reveals patterns, disparities, and relationships across geographic space, enabling evidence-based decisions in areas such as public health, urban planning, environmental management, economic development, and social services. When made openly available, this data can support transparency and foster accountability by allowing stakeholders to monitor developments, assess needs, and inform more inclusive policies. The ability to extract and use this data empowers a wide range of users—from government agencies and researchers to civil society organizations and citizens—to analyze spatial dynamics, generating deeper insights into complex societal challenges and opportunities.

Extracting open-source spatial data is rarely a straightforward task, due to the diversity of data types, formats, and platforms involved. Spatial data can include vector and raster formats, static and dynamic layers, and a wide range of thematic content, from land use and transportation networks to population distributions and environmental indicators. These datasets are hosted across various platforms, each with its own access protocols, licensing terms, and metadata standards. Users must navigate differences in spatial resolution, coordinate systems, and data structures, which can complicate integration and analysis. As a result, effective use of open spatial data requires not only technical proficiency but also a clear understanding of the data landscape and its limitations.

This document provides comprehensive guidelines for collecting open spatial data to support evidence-based decision-making across various sectors and applications. It outlines standardized methodologies, quality assurance processes, and technical specifications essential for gathering reliable, consistent spatial data. These guidelines are designed to ensure that data collection efforts yield high-quality, interoperable datasets that can be effectively integrated and analyzed to address diverse analytical needs.

The guidelines serve multiple audiences, including government agencies, international development organizations, research institutions, private sector entities, and civil society organizations. By standardizing spatial data collection practices, these guidelines enable more efficient resource utilization, reduce duplication of efforts, and facilitate data sharing and collaboration across institutional boundaries.

Open data serves as a fundamental catalyst for sustainable development and innovation. By making spatial information accessible, verifiable, and usable, open data illuminates previously invisible patterns and relationships that can inform better policies and interventions. Whether analyzing access to healthcare facilities, mapping environmental hazards, understanding urban growth patterns, or assessing infrastructure needs, standardized spatial data collection enables more precise and effective responses to societal challenges.

The principles and practices outlined in this document directly support multiple Sustainable Development Goals (SDGs). High-quality spatial data underpins efforts to reduce inequalities (SDG 10), build sustainable cities and communities (SDG 11), take climate action (SDG 13), and strengthen institutions (SDG 16). Moreover, the open data approach itself embodies the collaborative spirit of SDG 17 (Partnerships for the Goals), facilitating knowledge sharing and capacity building across regions and sectors.

Spatial data collection represents a unique intersection of geographic information science, domain expertise, and technological capability. The complexity of modern spatial analysis requires diverse data types from multiple sources, collected and processed using standardized methods that ensure compatibility and reliability. By following these protocols, stakeholders can build robust spatial data infrastructures that support evidence-based decision-making, enable monitoring and evaluation of interventions, and ultimately contribute to more equitable and sustainable development outcomes.

2. Open Data Principles and Standards

Open geospatial data is governed by a set of principles and standards designed to ensure that data is accessible, usable, and interoperable across platforms and applications. These principles guide how spatial data should be discovered, accessed, and utilized to maximize its value for analysis and decision-making.

2.1. FAIR Data Principles for Spatial Data

The FAIR principles provide a framework for ensuring that spatial data can be effectively discovered and used. Here's how they apply specifically to geospatial information:

FAIR Principle	What It Means	Spatial Data Requirements	Practical Example
Findable	Data can be easily discovered by humans and machines	• Include coordinate reference system (CRS) in metadata • Document geographic extent (bounding box) • Specify spatial resolution/scale • Use standardized geographic keywords	A dataset of health facilities includes metadata showing it covers Kenya, uses WGS84 coordinates, and contains point locations at 1:50,000 scale
Accessible	Data can be retrieved using standard protocols	• Provide data through OGC web services (WFS, WMS, WCS) • Offer downloads in common GIS formats • Include clear access instructions • Document any access restrictions	Users can download road network data as Shapefile or GeoJSON, or access it via a Web Feature Service with documented endpoints
Interoperable	Data works well with other datasets	• Use standard coordinate systems • Apply consistent boundary definitions • Follow established feature classifications • Document data structure clearly	Administrative boundaries use standard ISO codes and match official national boundary files, enabling easy joining with census data
Reusable	Data can be used for different purposes	• Include spatial accuracy information • Document collection methods and date • Specify appropriate usage scales • Provide clear licensing terms	Satellite-derived land cover includes accuracy assessment, collection date, recommended zoom levels, and CC-BY license

2.2. Open Data Charter Principles for Geographic Information

The Open Data Charter principles take on specific meanings when applied to spatial data:

Principle	Core Concept	Spatial Data Application	Implementation Tips
Open by Default	Data should be open unless there's a good reason not to be	• Publish all non-sensitive geographic data • Aggregate sensitive locations appropriately • Default to open licenses for government spatial data	Aggregate individual addresses to neighborhood level; publish infrastructure locations openly
Timely and Comprehensive	Current and complete coverage	• Cover entire geographic areas without gaps • Update regularly based on change frequency • Include all relevant features, not just urban areas	Ensure rural areas are mapped; update annually for slow-changing features, monthly for dynamic data
Accessible and Usable	Easy to access and work with	• Provide in standard GIS formats (Shapefile, GeoJSON, GeoTIFF) • Include in both proprietary and open-source compatible formats • Offer different scales/resolutions for different uses	Offer simplified versions for web visualization and detailed versions for analysis
Comparable and Interoperable	Can be compared and combined	• Use consistent spatial units across datasets • Standardize geographic classifications • Align to common boundary files	All datasets use the same district boundaries; coding schemes match national standards
For Improved Governance & Citizen Engagement	Support better decisions and participation	• Prioritize data revealing service gaps • Enable spatial analysis of inequalities • Support evidence-based planning	Publish facility locations to show underserved areas; enable distance-to-service analysis
For Inclusive Development and Innovation	Enable broad usage and new applications	• Remove technical barriers to access • Provide examples and documentation • Support diverse use cases	Include tutorials; provide API access; offer multiple formats

2.3. Data Quality Standards

When discovering and using spatial open data, understanding quality standards helps assess fitness for purpose:

Quality Dimension	What to Check	Why It Matters	Red Flags
Positional Accuracy	How precisely features are located	Determines suitable analysis scale	No accuracy statement; obvious misalignments
Attribute Completeness	Whether all fields contain data	Missing data limits analysis options	Many null values; undocumented codes
Temporal Currency	How recent the data is	Affects relevance for current decisions	No collection date; data >5 years old for dynamic features
Logical Consistency	Whether data follows its own rules	Indicates data reliability	Overlapping polygons; disconnected road networks
Lineage	How data was created/processed	Helps assess appropriateness	No methodology documentation; unclear sources

2.4. Metadata Standards for Spatial Data

Comprehensive metadata is essential for understanding and properly using spatial data:

Metadata Element	Description	Why It's Important	Minimum Requirement
Geographic Extent	Bounding coordinates or area covered	Determines if data covers area of interest	Bounding box coordinates or place names
Coordinate System	CRS/projection information	Required for accurate overlay with other data	EPSG code or full CRS definition
Data Structure	Feature types and attributes	Understand what's included and how it's organized	List of layers/tables and key fields
Collection Method	How data was gathered	Assess reliability and limitations	Basic method (survey, satellite, etc.)
Update Frequency	How often data is refreshed	Plan for data currency needs	Statement of update schedule or "static"
Access Information	How to obtain the data	Enable data retrieval	Download URL or service endpoint
Use Constraints	Any limitations on use	Ensure compliance and appropriate use	License type or "no restrictions"

3. Data Extraction Methodology

This chapter outlines comprehensive approaches for extracting open spatial data, focusing on methodologies that ensure high-quality, standardized geographic information for various analytical purposes. The extraction process encompasses discovering, accessing, evaluating, and acquiring spatial data from diverse sources while navigating technical and administrative challenges.

3.1. Data Sources Identification

Effective spatial analysis requires integrating data from multiple sources to create a comprehensive geographic understanding of the phenomena being studied. Each source type offers unique advantages and challenges in terms of coverage, quality, accessibility, and update frequency.

3.1.1. Official Statistics

Official statistics from government sources provide authoritative data with defined administrative boundaries and standardized collection methodologies. However, accessing this data often involves navigating significant administrative and technical challenges.

Key sources include:

National statistical offices
Census bureaus
Sectoral ministries (health, education, infrastructure, transportation, agriculture)
Regional and local government agencies
National mapping and cadastral agencies
Environmental protection agencies
Electoral commissions

Best practices:

Obtain data at the smallest available administrative level (ideally district or sub-district)
Document official geographic boundary definitions used by the source
Verify the coordinate reference system employed
Check update frequency and most recent collection date

Figure xx. Diagram showing typical government data flow from collection to publication, with emphasis on points where spatial references are added

Examples of official statistics with spatial components:

Data Type	Spatial Resolution	Typical Update Frequency	Common Spatial Identifier
Census data	Enumeration area	5-10 years	Census block ID
Labor force surveys	Administrative district	1-2 years	District code
Infrastructure registries	Exact coordinates	Variable	Latitude/longitude
Service location directories	Address or coordinates	Annual	Facility ID with coordinates
Environmental monitoring	Station points	Real-time to monthly	Station coordinates
Land use/zoning	Parcel level	Quarterly to annual	Parcel ID with geometry
Health statistics	Health district	Monthly to annual	Health facility code
Education facilities	School location	Annual	School ID with coordinates

Access Challenges and Navigation Strategies:

Given that we are discussing spatial data extraction, it is essential to examine the process in the specific context of government data sources. Accessing such data often requires navigating a range of administrative and procedural hurdles:

Challenge Category	Specific Issues	Impact on Data Extraction	Mitigation Strategies
Access Requirements	• User registration with email verification • Official institutional credentials • Formal request letters • Proof of research purpose • Government-to-government agreements	Delays project timelines; may exclude independent researchers	• Start registration early • Partner with recognized institutions • Prepare documentation templates • Build relationships with data officers
Technical Barriers	• Platform-specific software requirements • Limited API access or rate limits • CAPTCHA systems preventing automation • Session timeouts during large downloads • Browser-specific compatibility issues	Increases technical complexity; requires manual intervention	• Test platform requirements • Develop workarounds • Use official tools when required • Plan for manual processes
Format Inconsistencies	• Data in non-machine readable PDFs • Scanned documents requiring OCR • Interactive dashboards with no export • Mixed formats across regions • Proprietary database formats	Significant processing overhead; potential data loss	• Budget extraction time • Acquire necessary tools • Develop conversion pipelines • Document all transformations
Data Fragmentation	• Separate portals for each ministry • Different systems for each admin level • Historical data in different locations • Spatial and attribute data separated • No unified metadata catalog	Complicates comprehensive collection; increases integration effort	• Map all relevant portals • Create source inventory • Develop integration plan • Track data lineage

Practical Example: Extracting Health Facility Data

Scenario: Accessing public health facility locations from government sources

Typical Process:

Ministry website → Register account (2-3 days approval)
Navigate to data section → Find only PDF reports
Request spatial data → Directed to different department
Submit formal request → Wait 2-4 weeks
Receive data in mixed formats:
- Capital region: Shapefile with coordinates
- Other regions: Excel with addresses only
- Rural areas: PDF lists with village names
Integration required: Geocoding, digitization, harmonization

Best practices for navigating government data systems:

Pre-extraction Assessment:
- Survey all potential government sources
- Document access procedures for each
- Identify required credentials or permissions
- Note available formats and download options
- Check for usage restrictions or licenses
Common Spatial Data Types and Typical Access Patterns:

Data Type	Typical Provider	Common Formats	Spatial Reference	Access Complexity
Census boundaries	Statistics office	Shapefile, KML, PDF maps	Usually included	Medium - often requires registration
Demographic data	Census bureau	CSV, Excel, PDF tables	Admin codes only	High - may need special approval
Facility locations	Sectoral ministries	Excel, PDF, Web maps	Mixed quality	High - often fragmented
Infrastructure networks	Transport ministry	CAD files, PDFs	Variable	Very high - technical barriers
Land records	Cadastral agency	Proprietary GIS, PDF	Good quality	High - restricted access

Documentation Throughout the Process:
- Screenshot access procedures
- Save all correspondence
- Record exact download dates and URLs
- Note any data transformations required
- Maintain version control
- Document all license terms

3.1.2. International Databases

International organizations maintain standardized spatial datasets that enable cross-country comparison and provide data where national statistics may be unavailable. Where possible, users should consult existing manuals and official user guides that detail extraction methods for these sources. These resources provide valuable dataset-specific instructions, metadata documentation, and appropriate extraction techniques tailored to each platform's unique structure and access protocols.

Key spatial data sources:

Data Source	Description	Spatial Data Types	Common Applications	User Guide/Documentation
World Bank Open Data https://data.worldbank.org/	Development indicators and statistics for countries worldwide	Country-level data with some subnational coverage	Economic analysis, poverty mapping, development planning	World Bank Data Help Desk
UN Data Portal https://data.un.org/	Official UN statistics across multiple domains	National and some regional statistics	SDG monitoring, demographic analysis, social indicators	UNdata User Guide
Humanitarian Data Exchange (HDX) https://data.humdata.org/	Crisis and humanitarian data from multiple organizations	Administrative boundaries, infrastructure, population	Emergency response, vulnerability assessment, humanitarian planning	HDX Quick Start Guide
Natural Earth Data https://www.naturalearthdata.com/	Public domain map datasets	Global coverage at multiple scales (1:10m, 1:50m, 1:110m)	Base mapping, cartography, reference layers	Natural Earth Quick Start
OpenStreetMap Data Extracts https://download.geofabrik.de/	Crowdsourced geographic data	Roads, buildings, POIs, land use	Infrastructure analysis, accessibility studies, urban planning	OSM Data User Guide
FAO GeoNetwork https://www.fao.org/geonetwork/	Agricultural and environmental data	Land use, soil, climate, agricultural statistics	Food security, agricultural planning, environmental assessment	FAO GeoNetwork Manual
NASA Earthdata https://earthdata.nasa.gov/	Satellite observations and derived products	Remote sensing imagery, climate data, land cover	Environmental monitoring, climate analysis, disaster response	Earthdata User Guide
SEDAC (Socioeconomic Data) https://sedac.ciesin.columbia.edu/	Population, sustainability, and environmental data	Gridded population, environmental hazards	Population distribution, risk assessment, urban studies	SEDAC User Guide
GADM Database https://gadm.org/	Administrative boundaries worldwide	Administrative boundaries at all levels	Spatial analysis framework, administrative mapping	Documentation on website
Global Forest Watch https://www.globalforestwatch.org/	Forest monitoring and land use change	Forest cover, deforestation alerts, land use	Environmental monitoring, conservation planning	GFW How-To Guide
WHO Global Health Observatory https://www.who.int/data/gho	Health statistics and information	Health facility locations, disease data	Health planning, epidemiology, service accessibility	GHO Data Portal Guide
WorldPop https://www.worldpop.org/	High-resolution population data	Gridded population distributions, demographics	Population analysis, service planning, accessibility studies	WorldPop Data Access Guide
UNEP Environmental Data https://wesr.unep.org/	Environmental statistics and indicators	Environmental quality, natural resources	Environmental assessment, policy planning	Platform-specific guides available
ILO Statistics https://ilostat.ilo.org/	Labor and employment data	National and regional employment statistics	Labor market analysis, economic planning	ILOSTAT User Guide
OECD Data https://data.oecd.org/	Economic and social statistics	National and regional indicators	Economic analysis, policy comparison	OECD.Stat User Guide

Best practices:

Consult platform-specific user guides and API documentation before beginning data extraction
Verify the spatial harmonization methods used for cross-country datasets
Document vintage of both data and geographic boundaries
Check for post-collection spatial adjustments or transformations
Assess geographic completeness, particularly for small nations, territories, or remote regions
Identify the methodology used for spatial disaggregation of national statistics
Review data licenses and citation requirements for each source
Use bulk download options or APIs for large-scale data extraction when available

3.1.3. Crowdsourced Data

Participatory mapping and volunteered geographic information can fill critical gaps in official datasets, particularly for rapidly changing environments, areas with limited official coverage, or locally-specific features that may not appear in conventional datasets.

Understanding extraction pathways:

There are several ways to extract crowdsourced spatial data, each suited to different needs and technical capacities:

Extraction Method	Technical Level	Best For	Advantages	Limitations
Direct Downloads	Beginner	Complete datasets for specific regions	• Pre-processed files • Multiple formats available • No API knowledge needed	• Large file sizes • May include unnecessary data • Requires local processing
GIS Software Plugins	Intermediate	Specific features or small areas	• Query only needed data • Direct integration with analysis • Real-time access	• Requires GIS software • API limits may apply • Internet connection needed
APIs and Web Services	Advanced	Automated workflows, large-scale analysis	• Programmatic access • Always current data • Efficient for specific queries	• Programming skills required • Rate limits • Complex authentication
Curated Platforms	Beginner	Analysis-ready datasets	• Pre-cleaned data • Includes metadata • Quality controlled	• May not be fully current • Limited to available extracts • Less customizable

Key platforms and extraction methods:

Platform	Description	Extraction Methods	Documentation
OpenStreetMap (OSM)	Global crowdsourced map data	• Geofabrik downloads • Overpass API • Planet OSM files • QuickOSM (QGIS plugin)	OSM Wiki - Downloading Data
Geofabrik Downloads	Pre-extracted OSM data by region	• Direct downloads (PBF, Shapefile) • Updated daily	Geofabrik Download Server
HOT Export Tool	Custom OSM extracts with thematic filtering	• Web interface • Scheduled exports • Multiple formats	HOT Export Tool Documentation
Overpass Turbo	Query-based OSM data extraction	• Web interface • Custom queries • API access	Overpass API User's Manual
Mapillary	Street-level imagery and derived data	• API access • Web downloads • Developer tools	Mapillary Developer Guide
Local Ground	Community mapping platform	• Project-based exports • API access	Platform documentation
Ushahidi	Crisis mapping and crowdsourcing	• Platform exports • API access	Ushahidi Developer Documentation

Practical extraction example using different methods:

Task: Extract all health facilities in a district

Method 1 - Direct Download (Beginner):

Visit Geofabrik.de → Select country → Download shapefile
Load in GIS software → Filter by amenity=hospital/clinic
Clip to district boundary

Method 2 - QGIS Plugin (Intermediate):

Open QGIS → Install QuickOSM plugin
Query: amenity=hospital OR amenity=clinic
Set district as extent → Run query

Method 3 - Overpass API (Advanced):

[out:json];
area["name"="District Name"]->.searchArea;
(
node"amenity"~"hospital|clinic";
way"amenity"~"hospital|clinic";
);
out body;

Quality considerations for crowdsourced data:

Quality Aspect	What to Check	Validation Methods
Completeness	Coverage gaps, especially in rural areas	Compare with official registries, satellite imagery
Currency	Last edit dates, mapper activity	Check OSM metadata, changeset history
Accuracy	Positional accuracy, attribute correctness	Ground truthing, cross-reference with other sources
Consistency	Tagging variations, naming conventions	Data cleaning scripts, standardization tools

Best practices:

Choose the extraction method that matches your technical skills and data needs
Always check data currency using platform metadata or changeset information
Implement quality assurance protocols for volunteered geographic information
Document the extraction date, method, and any filters applied
Validate critical features against authoritative sources when available
Consider combining multiple crowdsourced platforms for better coverage
Review platform-specific tagging guides to understand data structure
Use established data models and schemas where available (e.g., OSM tagging conventions)

3.1.4. Satellite and Modeled Spatial Data Products

Earth observation and modeling techniques provide consistent spatial coverage across large areas, enabling analysis of physical features, environmental conditions, population distributions, and change over time. This section covers both direct satellite observations and derived/modeled products that combine satellite data with other inputs.

Understanding extraction pathways for satellite data:

Extraction Method	Technical Level	Best For	Infrastructure Needs	Documentation
Direct Download	Beginner-Intermediate	Small areas, specific dates	High bandwidth, storage	USGS EarthExplorer Guide
Cloud Platforms	Intermediate-Advanced	Large-scale analysis, time series	Internet connection, no storage	Google Earth Engine Guides
Desktop Plugins	Intermediate	Specific imagery, preprocessing	Local processing power	QGIS SCP Tutorial
APIs/Web Services	Advanced	Automated workflows	Programming skills	Platform-specific API docs
Data Cubes	Advanced	National-scale analysis	Significant infrastructure	Open Data Cube Manual

A. Raw Satellite Data Sources:

Satellite/Sensor	Spatial Resolution	Temporal Resolution	Key Applications	Access Methods	Documentation
Sentinel-2	10-60m	5 days	Land cover, vegetation, water	Copernicus Hub, GEE, AWS	Sentinel Online User Guide
Landsat 8/9	15-30m	16 days	Long-term change, thermal	USGS, GEE, AWS	Landsat User Guide
Sentinel-1	5-40m	6-12 days	Flood mapping, deformation	Copernicus Hub, GEE	Sentinel-1 User Guide
MODIS	250m-1km	Daily	Fire, temperature, vegetation	NASA Earthdata, GEE	MODIS Data User Guide
Planet	3-5m	Daily	High-res monitoring	Commercial API	Planet Developer Center
VIIRS	375-750m	Daily	Nighttime lights, fires	NOAA, NASA	VIIRS User Guide

B. Derived Satellite Products:

Product Category	Examples	Resolution	Update Frequency	Access Platform	Use Cases
Land Cover/Use	• ESA WorldCover • Dynamic World • MODIS Land Cover	10m-500m	Annual to real-time	GEE, Copernicus	Habitat mapping, urban growth, agricultural monitoring
Nighttime Lights	• VIIRS DNB • DMSP-OLS (historical)	500m-1km	Monthly	NOAA, GEE	Economic activity, electrification, urban extent
Vegetation Indices	• MODIS NDVI/EVI • Sentinel-2 vegetation	10m-1km	5-16 days	GEE, NASA	Agricultural monitoring, drought assessment, phenology
Water/Flood	• Global Surface Water • Sentinel-1 flood maps	10-30m	Event-based to annual	GEE, Copernicus EMS	Flood risk, water resources, disaster response
Elevation	• SRTM • ASTER GDEM • Copernicus DEM	30-90m	Static	USGS, Copernicus	Terrain analysis, watershed modeling, accessibility
Climate Variables	• CHIRPS rainfall • MODIS temperature	1-25km	Daily to monthly	Climate Data Store, GEE	Agricultural planning, climate risk assessment

C. Modeled Spatial Products (combining satellite with other data):

Product Type	Examples	Resolution	Methodology	Access	Applications
Population Distribution	• WorldPop • LandScan • GHS-POP • Facebook HRSL	30m-1km	Satellite + census + ML	HDX, WorldPop, GEE	Service planning, disaster response, demographic analysis
Infrastructure	• Global Roads (GRIP) • Building footprints • Global Power Plants	Various	Satellite + OSM + official	Direct download, HDX	Accessibility analysis, infrastructure planning
Urban Extent	• Global Human Settlement • World Settlement Footprint	10-30m	Satellite classification	GEE, DLR	Urban planning, growth monitoring
Environmental Risk	• Global Flood Database • Wildfire risk maps • Landslide susceptibility	Various	Satellite + modeling	Various platforms	Risk assessment, insurance, planning
Socioeconomic	• Relative Wealth Index • Grid3 settlements	Various	Satellite + ML + surveys	Meta, Grid3	Development planning, targeting interventions

Practical extraction workflows:

Example 1: Extracting Land Cover Data

Small area: Download from Copernicus Browser → Process in QGIS
Large area: Use GEE → Export to Google Drive
Time series: GEE or Open Data Cube → Cloud processing

Example 2: Population Distribution Analysis

Direct download: WorldPop.org → GeoTIFF files
Cloud analysis: GEE → WorldPop catalog
API access: WorldPop REST API → Custom queries

Quality considerations:

Data Type	Key Quality Checks	Validation Approaches
Optical Imagery	Cloud cover, atmospheric correction	Visual inspection, quality bands
Radar Data	Speckle noise, geometric distortion	Filtering, terrain correction
Derived Products	Classification accuracy, temporal consistency	Confusion matrices, ground truth
Modeled Data	Model assumptions, input data quality	Cross-validation, uncertainty maps

Best practices:

Choose appropriate spatial and temporal resolution for your analysis scale
Understand the difference between raw imagery and analysis-ready data
Document all preprocessing steps and product versions used
Consider seasonal and weather impacts on data quality
Validate satellite-derived products with ground data when possible
Review product-specific accuracy assessments and limitations
Use cloud platforms for large-scale processing to avoid data transfer
Combine multiple data sources to overcome individual limitations

3.2. Data Quality Assessment

Systematic quality assessment is essential when using open spatial data. Understanding common quality issues helps you identify potential problems early and make informed decisions about how to use the data appropriately. This section raises awareness of typical challenges without prescribing specific solutions, as the best approach depends on your particular context and analytical needs.

3.2.1. Accuracy and Reliability

Spatial data accuracy involves two main components that beginners should understand:

Positional accuracy: How precisely features are located on the map (are things in the right place?)
Attribute accuracy: How correct the information attached to features is (is the information about each feature correct?)

Common accuracy issues to watch for:

Figure xx. Diagram illustrating common spatial accuracy issues and their potential impact on spatial analysis

The diagram above shows four typical accuracy problems you might encounter:

Positional Inaccuracy: When recorded locations don't match true positions
- Example: A hospital marked 500m from its actual location
- Impact: Distance calculations and service area analysis become unreliable
Boundary Misalignment: When administrative or feature boundaries don't match reality
- Example: District boundaries from different sources don't align
- Impact: Data gets assigned to wrong areas, creating false patterns
Attribute Inaccuracy: When feature information is wrong or unclear
- Example: A school misclassified as a hospital in the data
- Impact: Analysis of available services becomes incorrect
Scale Inconsistency: When data detail varies across your study area
- Example: Urban areas mapped at building level, rural areas only at village level
- Impact: Some areas appear to have more features simply due to mapping detail

Simple ways to check accuracy:

What to Check	How to Check It	What You're Looking For	Why It Matters
Location accuracy	• Compare with known landmarks • Check against satellite imagery • Look for obvious errors (facilities in water)	Features should be reasonably close to expected positions	Wrong locations affect all distance-based analysis
Information accuracy	• Compare feature names/types with local knowledge • Check for duplicate entries • Look for missing or nonsensical values	Information should match what you know about the area	Wrong information leads to wrong conclusions
Logical consistency	• Check if roads connect properly • Verify administrative units don't overlap • Ensure features are in correct boundaries	Data should follow logical rules	Inconsistencies suggest data processing errors
Source credibility	• Check who created the data • Look for documentation • Note the data collection date	Authoritative or well-documented sources	Helps assess overall trustworthiness

Understanding accuracy terminology (for beginners):

RMSE (Root Mean Square Error): A measure of average position error - think of it as "typical distance off"
Ground truth: Real-world verification of what the data shows
Validation: Checking data against a trusted source
Anomaly: Something unusual that might indicate an error

Practical tips for beginners:

Start with visual checks: Load your data in GIS software and look for obvious problems
- Do features appear where you expect them?
- Are there gaps or clusters that seem wrong?
- Do boundaries make sense?
Use multiple sources: When possible, compare data from different sources
- If they agree, confidence increases
- If they disagree, investigate further
Document what you find: Keep notes about:
- Which datasets you checked
- What issues you discovered
- How you decided to handle them
Accept imperfection: No dataset is perfect
- Understand the limitations
- Decide if the quality is "good enough" for your purpose
- Be transparent about known issues

Questions to ask yourself:

Is this data accurate enough for my analysis needs?
What are the consequences if some locations or attributes are wrong?
Can I verify critical features through other means?
Should I collect additional data to fill quality gaps?

Remember: The goal isn't to achieve perfect data (which rarely exists) but to understand your data's limitations and work appropriately within them. Quality assessment builds your confidence in knowing when and how to use the data effectively.

3.2.2. Completeness

Completeness is about whether your spatial data tells the whole story. It involves two key questions:

Geographic coverage: Are all areas in your study region included?
Feature completeness: Are all relevant features captured in the data?

Why completeness matters: Missing data can lead to biased analysis and poor decisions. For example, if rural health facilities are missing from your dataset, any analysis will unfairly represent rural areas as underserved.

Common completeness issues to watch for:

Geographic gaps: Some areas might have no data at all
- Remote or rural areas often have less complete data
- Border regions may be missed due to administrative divisions
- Islands or isolated communities frequently lack coverage
Feature gaps: Important features might be missing
- Informal facilities/services often not captured in official data
- Recently built infrastructure not yet included
- Temporary or seasonal features overlooked
Inconsistent coverage: Data quality varies across regions
- Urban areas typically have more complete data
- Some districts may have better data collection than others
- Historical areas might have outdated or missing information

Figure xx. Map showing spatial data completeness assessment for a sample dataset, highlighting gaps in coverage

Simple ways to check completeness:

Check Method	What to Do	What to Look For	Example
Visual inspection	Display data on a map	Obvious gaps or empty areas	No roads shown in certain districts
Administrative comparison	Check each admin unit has data	Missing districts or regions	3 out of 20 districts have no health facility data
Local knowledge	Ask people familiar with the area	Known features not in dataset	Major market not appearing in commercial data
Coverage statistics	Calculate data density by area	Significant variations	Urban areas: 50 features/km², Rural: 0.5 features/km²
Multiple sources	Compare different datasets	Features in one but not another	OSM has schools that government data lacks

Documenting completeness (simple approach):

When you assess completeness, keep track of what you find:

Geographic Area	Data Theme	What's Missing	Why It Matters	Possible Solutions
Northern Region	Schools	Rural schools	Underestimates education access	Check education ministry data
Coastal Areas	Roads	Unpaved roads	Misrepresents connectivity	Use satellite imagery or local mapping
City Center	Businesses	Informal shops	Economic activity understated	Field survey or crowdsourced data
Mountain District	Health facilities	Mobile clinics	Health access appears worse than reality	Contact health department

Practical tips for dealing with incomplete data:

Be transparent: Always note where data is incomplete
- "This analysis only includes formally registered facilities"
- "Rural areas may be underrepresented in this dataset"
Understand the impact: Consider how gaps affect your analysis
- Will missing data change your conclusions?
- Are the gaps in critical areas for your study?
Explore solutions based on your resources:
- No additional resources: Work with what you have, but document limitations
- Some time available: Seek supplementary data from other sources
- Resources for fieldwork: Consider targeted data collection for critical gaps
Use completeness as a filter: Sometimes it's better to limit analysis to well-covered areas
- Analyze only regions with >80% coverage
- Focus on urban areas if rural data is too sparse

Questions to guide your assessment:

Where are the obvious gaps in coverage?
Why might these gaps exist? (accessibility, administrative issues, recent changes)
How critical are the missing areas/features to your analysis?
What's the minimum completeness level acceptable for your purpose?
Can you obtain supplementary data for critical gaps?

Remember about completeness:

Perfect completeness is rare - most datasets have gaps
Urban bias is common - expect better coverage in cities
Official data often misses informal/temporary features
Completeness can vary by theme (roads may be complete while buildings are not)
Document what's missing as thoroughly as what's present

The goal is not to achieve 100% completeness but to understand what's missing and how it affects your analysis. This awareness helps you make informed decisions and communicate limitations honestly.

3.2.3. Temporal Resolution

Temporal resolution refers to how current your data is and how often it gets updated. Using outdated data can lead to incorrect conclusions, especially for features that change frequently.

Why timing matters: The world changes constantly - new roads are built, facilities open or close, populations shift. Understanding how fresh your data needs to be depends entirely on what you're analyzing.

Common temporal issues to consider:

Data Type	Typical Change Rate	Ideal Update Frequency	Impact of Outdated Data
Demographic data	Slow	1-5 years	Population estimates become less accurate
Infrastructure	Moderate	6 months - 2 years	Missing new developments, closed facilities
Transportation	Variable	Monthly - Annual	Route changes, service updates missed
Emergency services	Fast	Real-time - Monthly	Critical service availability incorrect
Land use	Moderate	1-3 years	Urban expansion not captured
Economic activity	Fast	Monthly - Quarterly	Business closures, market changes missed

Figure xx. Timeline visualization showing ideal temporal resolution for different data types

Simple ways to check temporal quality:

Look for date stamps: When was the data collected or last updated?
- Check metadata for collection dates
- Look for "last modified" information
- Note any version numbers
Consider the context: How fast do things change in your study area?
- Urban areas typically change faster than rural
- Developing regions may have rapid infrastructure changes
- Post-disaster areas need very current data
Identify seasonal patterns: Some features vary by season
- Road accessibility in rainy seasons
- Seasonal businesses or services
- Agricultural land use changes

Questions to guide your assessment:

How old is too old for my analysis purpose?
What features in my area change most rapidly?
Are there recent events that would make older data obsolete?
Does my analysis period match my data period?

Practical tips for temporal issues:

Date your analysis: Always state when your data was collected
- "Based on 2023 road network data"
- "Population figures from 2020 census"
Mix time periods carefully: When combining datasets
- Document different collection dates
- Consider if changes between dates affect results
- Avoid comparing different time periods directly
Update critical data: Prioritize updating frequently-changing features
- Emergency services for safety analysis
- Transportation for accessibility studies
- Current businesses for economic analysis

Red flags for temporal problems:

No date information available
Data more than 5 years old for dynamic features
Major events (disasters, construction projects) since data collection
Inconsistent dates across related datasets

Remember: The "right" update frequency depends on your use case. Census data from 5 years ago might be acceptable for general planning, but emergency service locations from last year could be dangerously outdated. Always consider how data age affects your specific analysis goals.

3.2.4. Spatial Resolution

Spatial resolution refers to the level of detail in your data - how small are the units used to represent geographic features? This determines what patterns you can see and what might be hidden.

Why resolution matters: Think of spatial resolution like zoom levels on a map. At low resolution (zoomed out), you see general patterns. At high resolution (zoomed in), you see local details. The right resolution depends on what questions you're asking.

Understanding different resolution levels:

Spatial Resolution	What You Can See	Best Used For	Trade-offs	Common Examples
Building/Parcel	Individual structures	Detailed neighborhood analysis	• Hard to get • Privacy issues • Large file sizes	Building footprints, property maps
Block/Village	Small area patterns	Community planning	• Good detail • Manageable size • Some boundaries unclear	Census blocks, neighborhood data
District/Municipality	Area-wide trends	Local government planning	• Matches admin units • Hides local variation	Municipal statistics, service areas
Province/State	Regional patterns	Policy making	• Complete coverage • Too general for local needs	National surveys, regional data

Figure xx. Multi-scale visualization of the same area showing how different spatial resolutions reveal or obscure patterns

Common resolution concepts explained simply:

Minimum mapping unit: The smallest feature that appears in your data
- Example: If buildings smaller than 50m² aren't included, that's your minimum
- Why it matters: Small but important features might be missing
Spatial patterns across scales: How patterns change at different resolutions
- Example: Poverty might look evenly distributed at province level but clustered at neighborhood level
- Why it matters: Coarse resolution can hide important local variations
Scale-appropriate representation: Using the right detail level for your purpose
- Example: City-wide planning doesn't need individual building details
- Why it matters: Too much detail can be overwhelming; too little hides important patterns
Resolution matching: Making sure different datasets work together
- Example: Combining neighborhood-level health data with district-level population data
- Why it matters: Mismatched resolutions create analysis problems
Aggregation effects: How combining smaller units into larger ones changes the picture
- Example: Average income by district versus by neighborhood
- Why it matters: Aggregation can hide pockets of need or opportunity

Practical considerations:

Choosing the right resolution:

What decisions will be made with this analysis?
Who needs to use the results?
What's the smallest area that matters for action?
What data is actually available?

Common resolution mismatches and solutions:

Situation	Problem	Practical Solution
Population data by district, facilities as points	Can't calculate facility density accurately	Aggregate facilities to districts or find finer population data
High-res satellite imagery, coarse admin boundaries	Imagery detail wasted	Consider creating finer analysis zones
Mixed urban (detailed) and rural (coarse) data	Uneven analysis quality	Document the difference, analyze separately
Different years at different resolutions	Changes confused with resolution effects	Use consistent resolution across years

Questions to ask yourself:

Is my data resolution appropriate for my analysis goals?
Am I seeing real patterns or just resolution effects?
Where might important details be hidden by coarse resolution?
Do all my datasets have compatible resolutions?

Tips for working with resolution:

Start by understanding what resolution you have (check metadata)
Document any resolution conversions you make
Be honest about what your resolution can and cannot show
Consider showing results at multiple resolutions when possible
Remember: finer resolution isn't always better - it depends on your purpose

Red flags:

Trying to make local decisions with regional data
Combining incompatible resolutions without acknowledgment
Assuming fine resolution data is automatically more accurate
Ignoring how aggregation might hide important variations

The key is matching your data resolution to your analysis needs while being transparent about what details might be missed at your chosen scale.

3.3. Data Extraction Tools and Technologies

Choosing the right tools and file formats can make the difference between a smooth workflow and hours of frustration. This section helps you select appropriate technologies for extracting and working with spatial data.

3.3.1. Common Spatial File Formats

Different file formats serve different purposes. Choosing the right one depends on your data type, intended use, and the software you're using.

Understanding spatial data formats:

Format	What It's For	Pros	Cons	When to Use
GeoJSON	Sharing vector data online	• Human-readable • Works in web browsers • Easy to edit	• Gets slow with big files • Only for vector data	Web mapping, data sharing, APIs
Shapefile	General vector data	• Works everywhere • Industry standard • Fast processing	• Multiple files (.shp, .dbf, .shx) • 10-character field names • 2GB size limit	Desktop GIS analysis, data exchange
GeoTIFF	Raster/image data	• Keeps location info • Compression options • Widely supported	• Can be very large • Only for raster	Satellite imagery, elevation data, continuous surfaces
GeoPackage	Modern all-purpose	• Everything in one file • No size limits • Vector and raster	• Newer format • Some software doesn't support	Complex projects, data sharing, mobile apps
CSV with coordinates	Simple point locations	• Opens in Excel • Very simple • Universal	• Only points • No projection info • Easy to break	Lists of locations, simple data transfer

Figure xx. Decision tree for selecting appropriate spatial data formats based on data characteristics and intended use

Quick format selection guide:

Ask yourself these questions:

Is your data vector (points, lines, polygons) or raster (images, grids)?
- Vector → Consider Shapefile, GeoJSON, GeoPackage
- Raster → Use GeoTIFF
Where will you use it?
- Web browser → GeoJSON
- Desktop GIS → Shapefile or GeoPackage
- Multiple platforms → GeoPackage
How big is your data?
- Small (<50MB) → Any format works
- Large (>1GB) → Avoid GeoJSON, consider GeoPackage
- Huge (>2GB) → Can't use Shapefile, need GeoPackage or database

Format conversion tips:

Most GIS software can convert between formats
Always check your data after conversion
Keep the original file as backup
Document what conversions you made

Common format problems and solutions:

Problem	Likely Cause	Quick Fix
Can't open file	Wrong format for software	Convert to supported format
Missing location	No projection info	Define projection in GIS
Broken characters	Encoding issues	Check UTF-8 encoding
File too large	Inefficient format	Compress or change format
Lost attributes	Format limitations	Check field name length

Best practices:

Choose formats based on your workflow needs, not just familiarity
Consider your collaborators' software capabilities
Document which format and projection you're using
Test format compatibility early in your project
Keep data in the simplest appropriate format

Remember: No format is perfect for everything. The "best" format depends on your specific needs, software, and sharing requirements.

3.3.2. Spatial Data Processing Software

Different software tools serve different purposes in spatial data work. This section helps you choose the right tool for your needs and skill level.

Desktop and analysis software:

Software	What It Does	Skill Level	Cost	Best For
QGIS	Complete GIS toolkit with visual interface	Beginner to advanced	Free	• Making maps • Basic to advanced analysis • Data viewing and editing
GDAL/OGR	Converts between formats, processes data via commands	Intermediate to advanced	Free	• Batch converting files • Automating repetitive tasks • Format troubleshooting
Python (GeoPandas, Rasterio)	Programming for custom analysis	Intermediate to advanced	Free	• Repeating analysis • Combining many datasets • Building custom tools
R (sf, terra)	Statistical analysis with maps	Intermediate to advanced	Free	• Statistical modeling • Research analysis • Data visualization
Google Earth Engine	Analyze satellite imagery online	Intermediate	Free	• Large area analysis • Time series • No download needed
PostgreSQL/PostGIS	Database for storing and querying spatial data	Advanced	Free	• Managing large datasets • Multi-user access • Complex spatial queries

Understanding PostgreSQL/PostGIS: Think of it as a filing cabinet for maps. Instead of having hundreds of files on your computer, you store everything in one organized system where you can quickly find what you need using searches like "show me all schools within 2km of a road."

Which desktop tool should you start with?

New to GIS? → Start with QGIS
Have programming experience? → Try Python or R
Working with satellite imagery? → Use Google Earth Engine
Managing lots of data for an organization? → Consider PostgreSQL/PostGIS

Mobile data collection tools:

Sometimes you need to collect data in the field to fill gaps or verify existing information. These tools help you do that:

Tool	Platform	Works Offline?	What It's Like	Use It For
ODK Collect	Android	Yes	Like a digital survey form	• Recording locations • Structured questionnaires • Photo documentation
KoboToolbox	Web, Android	Yes	User-friendly forms with analysis	• Field surveys • Monitoring visits • Data visualization
QField	Android	Yes	QGIS on your phone	• Editing existing maps • Professional data collection • Complex geometries
Mapillary	iOS, Android	Collect offline, process online	Street View-style photos	• Road conditions • Infrastructure inventory • Visual documentation
Epicollect5	iOS, Android	Yes	Simple and flexible	• Community mapping • Citizen science • Quick surveys

Understanding ODK Collect: Imagine a digital clipboard that knows your location. You create forms on a computer (like "Hospital Assessment"), then collectors use phones to fill them out in the field, automatically recording GPS locations. All responses sync to a central database when internet is available.

Figure xx. Field data collection workflow diagram, from planning to integration with existing datasets

Choosing mobile tools:

Ask yourself:

What am I collecting? Simple points → ODK/KoboToolbox; Complex mapping → QField
Who's collecting? Community members → Simple tools; GIS professionals → Advanced tools
Need photos? Documentation → Any tool; Street view → Mapillary
What happens to the data? Just viewing → Any tool; GIS analysis → QField/ODK

Practical workflow example:

Plan: Define what data you need
Design: Create forms/projects in chosen tool
Test: Try it yourself before deployment
Collect: Field teams gather data
Sync: Upload data when connected
Process: Clean and validate in QGIS
Integrate: Combine with existing datasets

Tips for software selection:

Start simple - you can always upgrade later
Test with a small pilot before full deployment
Consider your team's technical skills
Check if your existing data works with the tool
Look for active user communities for help

Remember: The best tool is the one your team can actually use effectively. Fancy features don't help if they're too complex for your situation.

3.4. Standardization and Harmonization Procedures

When combining spatial data from different sources, you need to ensure they all "speak the same language." This means using consistent formats, coordinate systems, and classification methods so your datasets work together properly.

3.4.1. Coordinate Reference Systems

Think of coordinate reference systems (CRS) like different ways of drawing the round Earth on flat maps. Each system has trade-offs, and using the wrong one can make your analysis incorrect.

Why this matters: If your datasets use different coordinate systems, features won't line up properly. Roads might appear to run through buildings, or distances might be wildly wrong.

Common coordinate system concepts (in plain language):

Geographic coordinates (lat/lon): Like a global address system using degrees
- Example: 40.7128°N, 74.0060°W (New York City)
- Good for: Storing locations, sharing data globally
- Bad for: Measuring distances or areas
Projected coordinates: Flatten the Earth for accurate measurements
- Example: UTM coordinates like 583960E, 4507523N
- Good for: Measuring distances, calculating areas
- Bad for: Large areas (distortion increases)

Key coordinate systems to know:

Purpose	System Name	Code	When to Use	Remember
Storing data	WGS84	EPSG:4326	Default for GPS, data sharing	Universal standard
Web maps	Web Mercator	EPSG:3857	Online mapping	Distorts sizes badly
Local analysis	UTM (your zone)	Varies	Distance measurements	Different zones for different regions
Area calculations	Local equal-area	Varies	Measuring land area	Preserves area, distorts shape

Figure xx. Map showing how coordinate system choice affects area and distance measurements in different regions

Simple checks for coordinate system issues:

Visual check: Load all datasets in GIS - do they line up?
Location check: Is your data in the right country/ocean?
Unit check: Are coordinates in degrees (-180 to 180) or meters (large numbers)?

Common coordinate system problems:

Problem	What You'll See	Solution
Missing CRS	Data appears in wrong location	Define the coordinate system
Wrong CRS	Data offset by hundreds of meters/miles	Reproject to correct system
Mixed systems	Layers don't align	Convert all to same system
Web vs. analysis	Measurements incorrect	Use appropriate system for task

Practical workflow:

Check what coordinate system each dataset uses
Choose appropriate system for your analysis:
- Just viewing? → Keep original
- Measuring distances? → Use projected system
- Combining datasets? → Convert all to same system
Document your choice
Transform datasets as needed
Verify alignment visually

Quick decision guide:

Sharing data internationally? → Use WGS84 (EPSG:4326)
Making a web map? → Convert to Web Mercator (EPSG:3857)
Measuring distances locally? → Use appropriate UTM zone
Calculating areas? → Use equal-area projection for your region

Tips for beginners:

Most GPS data comes in WGS84 - this is your starting point
Your GIS software can convert between systems (called "reprojecting")
When in doubt, WGS84 is the safest choice for storage
Always document which system you used
If data doesn't align, coordinate system mismatch is the likely culprit

Remember: There's no perfect coordinate system for everything. Choose based on what you need to do with the data, and be consistent across all your datasets.

3.4.2. Spatial Indexing and Aggregation Frameworks

Sometimes administrative boundaries (like districts or provinces) don't work well for analysis. They vary wildly in size, shape, and population. Spatial indexing systems offer an alternative: consistent, regularly-shaped units that make comparison and analysis easier.

What are spatial indexes? Think of them as graph paper laid over a map. Instead of irregular administrative units, you get uniform grid cells or hexagons. This makes it easier to:

Compare different areas fairly
Aggregate data consistently
Analyze patterns without boundary bias

Common spatial indexing systems:

System	Shape	Who Uses It	Best For	Simple Explanation
H3	Hexagons	Uber, data scientists	Smooth analysis	Like honeycomb cells covering the Earth
S2	Squares (curved)	Google	Big data	Squares that fit Earth's curve
Simple Grid	Squares	Anyone	Basic analysis	Regular graph paper grid
Quadkeys	Squares	Microsoft, web maps	Online maps	How web map tiles are organized

Figure xx. Comparison of H3 hexagons versus administrative boundaries for analyzing service access patterns

Why use spatial indexes instead of administrative boundaries?

Issue with Admin Boundaries	How Indexes Help
Vastly different sizes (tiny urban districts vs huge rural ones)	All cells are similar size
Irregular shapes make distance calculations complex	Regular shapes simplify analysis
Political boundaries change over time	Grid stays constant
Hard to compare densities across different areas	Equal areas make comparison fair

H3 Hexagon System (Most Popular for Analysis):

H3 is like laying hexagonal tiles over your map. You choose the tile size based on your needs:

Resolution	Cell Width	Area	Think of It As	Use When Analyzing
7	~1.2 km	5 km²	Large neighborhoods	City-wide patterns
8	~460 m	0.7 km²	Several city blocks	Neighborhood services
9	~174 m	0.1 km²	Single block	Local accessibility
10	~65 m	0.015 km²	Large buildings	Detailed urban features

When to use spatial indexing:

Comparing service access across a city
Analyzing population density fairly
Creating heat maps of activity
Standardizing data from different sources
Working across administrative boundaries

When to stick with administrative boundaries:

Your results need to match government units
You're working with official statistics
Decision-makers expect traditional boundaries
Data is only available by admin unit

Simple example: Instead of: "District A has 5 hospitals, District B has 2" (But District A is 10x larger!)

Using H3: "These hexagons average 0.8 hospitals per km², those hexagons average 1.2 hospitals per km²" (Fair comparison!)

Getting started:

Most indexing systems have online tools to generate grids
QGIS plugins available for H3 and simple grids
Start with resolution 8 for city-level analysis
Test different resolutions to find what works

Tips:

Hexagons (H3) are better for distance analysis than squares
Squares are simpler and more familiar to most users
You can always aggregate indexed data back to admin boundaries
Document which system and resolution you used

Remember: Spatial indexes are just another way to organize geographic data. Use them when they make your analysis clearer and fairer, but don't overcomplicate things if traditional boundaries work fine for your purpose.

3.4.3. Data Integration Methods

Once you have data from different sources, you need to combine them meaningfully. This section covers common methods and challenges you'll encounter when bringing datasets together.

Common ways to combine spatial data:

Spatial joins: Connecting data based on location
- Example: Which schools are in which districts?
- How: GIS software matches features by their position
Attribute matching: Using common identifiers
- Example: Joining census data to districts using district codes
- How: Match shared ID fields between datasets
Format conversion: Making different data types work together
- Example: Converting points to a grid for analysis with raster data
- How: Use GIS tools to transform between formats
Unit matching: Handling different spatial units
- Example: Population by district + services by neighborhood
- How: Aggregate or disaggregate to common units
Alignment fixing: Making misaligned boundaries match
- Example: Two datasets with slightly different coastlines
- How: Adjust boundaries to a common reference

Figure xx. Workflow diagram showing the integration process for combining different types of spatial data

Common integration challenges and practical solutions:

Challenge	What It Looks Like	Simple Solution	Remember to Document
Boundaries don't match	Features appear on wrong side of borders	Snap to official boundaries	Which boundary file you used
Different time periods	2020 population with 2023 facilities	Note the time difference	Date of each dataset
Different detail levels	City blocks vs whole districts	Aggregate to coarser level	What detail was lost
Different categories	"School" vs "Primary/Secondary"	Create matching table	How you grouped categories
Coverage gaps	Some areas have no data	Note gaps or estimate	Where data is missing

Understanding temporal issues (time differences):

Real-world data is collected at different times, and things change. As a beginner, you don't need complex adjustments, but you should:

Know your data dates: Always check when data was collected
Think about change: Has the area changed significantly since then?
Document differences: Note when datasets are from different years
Be transparent: Tell users about time gaps in your analysis

Simple example of time awareness: Population data: 2020 census School locations: 2023 survey → Note: "Population figures are 3 years older than facility data" → Consider: Have new neighborhoods been built since 2020?

Figure xx. Timeline showing how different datasets often come from different time periods

Basic temporal alignment approach:

When time differences matter, you can:

Use everything as-is: Often fine if changes are slow
Pick a reference year: Try to get all data close to one year
Update critical data: Refresh the most important/changeable datasets
Document clearly: Always note the time period of each dataset

Integration workflow checklist:

List all datasets and their formats
Check coordinate systems match
Note the date of each dataset
Identify common joining fields or locations
Test integration with a small sample
Document any transformations made
Check results make sense visually
Note any assumptions or limitations

Practical tips:

Start simple - join just two datasets first
Always keep original files unchanged
Document every step you take
Verify results visually in your GIS
When in doubt, note the limitation rather than hiding it

Red flags to watch for:

Features appearing in wrong locations after joining
Sudden changes in patterns at dataset boundaries
Missing data after integration (did the join fail?)
Unrealistic values after aggregation

Remember: Perfect integration is rare. The goal is to combine data thoughtfully, understand the limitations, and document what you did so others can evaluate your work.

bennyistanto/Guideline_on_GeospatialOpenData_Collection.md

WBG's Guideline on Geospatial Open Data Collection

1. Introduction

2. Open Data Principles and Standards

2.1. FAIR Data Principles for Spatial Data

2.2. Open Data Charter Principles for Geographic Information

2.3. Data Quality Standards

2.4. Metadata Standards for Spatial Data

3. Data Extraction Methodology

3.1. Data Sources Identification

3.1.1. Official Statistics

3.1.2. International Databases

3.1.3. Crowdsourced Data

3.1.4. Satellite and Modeled Spatial Data Products

3.2. Data Quality Assessment

3.2.1. Accuracy and Reliability

3.2.2. Completeness

3.2.3. Temporal Resolution

3.2.4. Spatial Resolution

3.3. Data Extraction Tools and Technologies

3.3.1. Common Spatial File Formats

3.3.2. Spatial Data Processing Software

3.4. Standardization and Harmonization Procedures

3.4.1. Coordinate Reference Systems

3.4.2. Spatial Indexing and Aggregation Frameworks

3.4.3. Data Integration Methods