In the 1980s, I began collecting geospatial data to support what would then turn out to be the first of two dissertation topics I pursued. I wanted to quantify forest transition models at different altitudes and chose the Sangre de Cristo mountains in New Mexico as a study site. The mountains are beautiful, extending over 10,000 feet in elevation. I established study sites at different elevations, going up in 1,000ft increments. It was a bracing environment, and I loved it. In the end, it took me all summer to collect a dataset.
Fast-forward to the present day and the world of data collection has changed completely.
Geospatial data forms the backbone of numerous industries and research fields, enabling insights into geographic trends, population dynamics, and environmental changes. This blog explores key methods for collecting geospatial data, focusing on satellite imagery, mobile devices, census data, geographic boundaries, and data related to populations, individuals, diseases, and environmental exposures.
1. Satellite Imagery
Satellite imagery is one of the most prominent sources of geospatial data. With this geospatial data collection method, satellites are equipped with advanced sensors to capture high-resolution images of Earth’s surface. The data from the satellite sensors can be used for various applications, including:
- Land use and land cover mapping: Identifying urban, agricultural, and natural areas.
- Environmental monitoring: Tracking deforestation, climate change impacts, and natural disasters.
- Urban planning: Assessing infrastructure development and spatial growth patterns.
Modern satellites, such as those operated by NASA, ESA, and private companies like Maxar Technologies, provide data in multiple spectral bands, enabling detailed analysis of the Earth’s surface.
2. Mobile Devices: Smartphones and Vehicles
Mobile devices have revolutionized geospatial data collection by making it real-time and highly granular. Smartphones and GPS-enabled vehicles have sensors that record location, speed, and environmental data.
Key applications include:
- Navigation and traffic management: Platforms like Google Maps and Waze use crowd-sourced location data to optimize routes and provide traffic updates.
- Geotagging: Social media platforms use GPS data to tag locations, contributing to datasets for local trends and events.
- Field surveys: Smartphones and drones allow researchers to collect geospatial data in remote or inaccessible areas.
3. Census Data
Census data serves as a fundamental source of demographic and socio-economic geospatial information. Governments and organizations conduct censuses to gather data on:
- Population distribution and density
- Employment, income, and education levels
- Housing and infrastructure
By mapping census data to geographic boundaries, policymakers can make informed decisions about resource allocation, urban planning, and public services.
4. Geographic Boundaries
Geographic boundaries define regions, from countries and cities to electoral districts and ecological zones. This data is crucial for spatial analysis and is collected using:
- Administrative records: Maps and datasets from government agencies.
- GPS surveys: Accurate boundary delineation using GPS devices.
- Historical records: Incorporating legacy maps into modern GIS systems.
Applications range from political redistricting to ecological conservation and disaster management.
5. Population-Level Data
Population-level geospatial data provides insights into trends affecting large groups of people. Sources include:
- Public health records: Tracking disease prevalence and vaccination coverage.
- Surveys and questionnaires: Collecting information on socio-economic indicators.
- Big data analytics: Aggregating anonymized data from smartphones, social media, and online platforms.
These data are invaluable for public health campaigns, infrastructure development, and policy formulation.
6. Individual-Level Data
Individual-level geospatial data focuses on the movements, behaviors, and interactions of single individuals.
Data collection methods include:
- Mobile apps: Fitness trackers, such as an Apple Watch, and location-based services. When an app asks to use your location, that’s an example of a geospatial data collection method!
- Wearable devices: Sensors that monitor physical activity and environmental exposure.
- Interviews and diaries: Traditional methods for capturing individual-level spatial behaviors.
This data is critical for personalized healthcare, behavioral research, and marketing strategies.
7. Disease Data
Geospatial data is essential for understanding the spread and impact of diseases. It supports efforts to:
- Map outbreaks: Identifying hotspots for diseases like malaria or COVID-19.
- Monitor healthcare access: Assessing disparities in medical services.
- Predict trends: Using spatial modeling to forecast disease dynamics.
Data sources include health records, satellite imagery for environmental factors, and mobile tracking during epidemics.
8. Environmental Exposures
Environmental exposures such as air pollution, water quality, and climate-related hazards significantly impact public health and ecosystems. Geospatial data collection methods for environmental exposures include:
- Satellite and drone imagery: Monitoring land, water, and air quality.
- Sensor networks: Collecting real-time environmental data at a local level.
- Crowd-sourced data: Contributions from citizen science projects, such as tracking pollution levels.
Geospatial analysis of this data helps identify vulnerable areas and supports mitigation strategies.
9. Shoe Leather Data Collection
Well, not an actual leather shoe.
Shoe leather data collection refers to methods where individuals actively gather geospatial data through on-the-ground efforts. This approach emphasizes first-hand observation and manual data recording. This of course is where this blog started with the author’s experiences in the Sangre de Cristo.
Key approaches include:
- Field surveys: Walking or driving through areas to record geographic features, environmental conditions, or human activities.
- Citizen Science: Engaging community members to collect data on topics such as biodiversity, pollution, and local weather conditions. Examples include bird-watching programs or air quality monitoring projects.
- Manual mapping: Drawing or updating maps based on observations, often used in disaster response or rural development projects.
Shoe leather data collection empowers individuals to contribute to larger datasets and fosters a deeper understanding of local environments and communities.
Geospatial Data Collection Methods For Present-Day Geostaticians
Geospatial data collection is a multidisciplinary effort that leverages technology, community participation, and traditional methods. Whether through satellite imagery, mobile devices, census records, or environmental monitoring, the richness of geospatial data enables smarter decisions and more sustainable outcomes across various fields. By continually advancing data collection methods, we can better understand and address the complex challenges of today’s world.