Remark
Please be aware that these lecture notes are accessible online in an ‘early access’ format. They are actively being developed, and certain sections will be further enriched to provide a comprehensive understanding of the subject matter.
3.5. Creating New Geospatial Data#
Despite the abundance of existing geospatial data sources, there are numerous scenarios where the creation of new, custom geospatial data becomes necessary. These situations may arise due to several factors:
Absence of data for specific geographic areas or phenomena [Goodchild, 2007]
Requirement for more current or up-to-date information [Longley et al., 2015]
Need for higher spatial resolution or improved accuracy compared to available datasets [Longley et al., 2015]
Unique project requirements that cannot be met by existing data sources [Elwood, 2008]
The creation of new geospatial data enables researchers and practitioners to address gaps in existing datasets, verify or update outdated information, and develop bespoke datasets tailored to specific project needs. This process encompasses a range of methodologies, including field surveys, remote sensing techniques, digitization of analog maps, and derivation from existing digital data.
When creating new geospatial data, several methods can be employed, each with its own strengths and suitability for different tasks. Here’s a brief overview of each method:
3.5.1. Digitization of Analog Maps#
This process involves converting physical maps or other analog spatial data into digital format. This can be done using tools like digitizing tablets, where users manually trace map features to create digital representations. This method is useful for updating old maps or incorporating historical data into modern GIS systems [Burt et al., 2020, Goodchild, 2007, Mosbah et al., 2024, Villar-Cano et al., 2019, Werner and Chiang, 2021].
3.5.2. Processing of Remote Sensing Data#
Remote sensing involves gathering data about the Earth’s surface from a distance. This data can include optical, LiDAR (Light Detection and Ranging), SAR (Synthetic Aperture Radar), and other types of imagery. Remote sensing data is processed to extract relevant information such as land cover, elevation, and other environmental attributes. This method is highly effective for large-scale data collection and monitoring changes over time [Ban, 2016, Long et al., 2018, Thenkabail, 2018].
3.5.3. GPS Data Collection#
Global Positioning System (GPS) data collection involves using GPS devices to capture the precise locations of points, lines, or polygons on the Earth’s surface. This method is commonly used for field surveys, tracking movements, and creating detailed maps of specific areas. GPS data can be integrated into GIS software for further analysis and visualization [Dolins et al., 2021].
3.5.4. Field Surveys#
Field surveys involve collecting data directly in the field using various tools and techniques. This can include observations, measurements, and interviews. Field surveys are essential for gathering detailed, ground-truth data that can be used to validate remote sensing data or fill gaps in existing datasets. They are particularly useful for projects requiring high accuracy and detailed local information.
3.5.5. Geocoding#
Geocoding is the process of assigning geographic coordinates (latitude and longitude) to address data or other spatially referenced information. This method is crucial for converting non-spatial data into spatial data that can be analyzed and visualized using GIS. Geocoding can be automated using software tools and is widely used in urban planning, logistics, and emergency services.
3.5.6. Interpolation#
Interpolation involves estimating values for unknown locations based on data from known locations. This technique is used to create continuous surfaces from discrete data points, such as elevation models or climate data. Common interpolation methods include inverse distance weighting, kriging, and spline interpolation. Interpolation is useful for filling gaps in data and creating smooth, continuous datasets.
3.5.7. Derivation from Existing Data#
Deriving new geospatial data from existing datasets involves manipulating or combining existing data to create new information. This can include spatial joins, overlays, and other spatial analysis techniques. For example, combining land use data with demographic data can provide insights into urban planning and resource allocation. This method leverages the wealth of existing geospatial data to generate new, tailored datasets.
Each of these methods has its specific applications and advantages, and choosing the right method depends on the project’s requirements, the availability of resources, and the desired level of accuracy and detail.