Why using polygons instead of points is better for land analysis?
Land Insight uses data analytics and geospatial technology to map, identify and catalog a range of current and historical land use practices, allowing us to create the biggest dataset of land use in Australia.
Since its inception, we have been digitising and creating all our datasets in polygons to represent the true shape and geometry of the land change, regardless of whether it is a land contamination, landfill, or a derelict mine.
Using polygons instead of points is better for geospatial analysis because polygons provide a more detailed and accurate representation of the spatial extent and boundaries of a geographic feature. Polygons allow for more complex and precise analyses such as area measurements, spatial relationships between features, and spatial patterns of distribution.
Additionally, polygon data can be used in conjunction with other geospatial data, to perform more advanced analyses such as land cover classification and change detection.
Using point data instead of polygon data to represent land changes can result in several major issues, including:
1. Loss of spatial information: Point data represents a single location and does not capture the spatial extent or boundaries of the land change. This can lead to a loss of important spatial information and can result in less accurate analysis.
2. Inability to capture changes in land use/land cover: Point data only provides information on the location of the change and not the extent or nature of the change. As a result, it cannot capture changes in land use or land cover, which are critical for many environmental and land management applications.
3. Difficulty in measuring area and volume: Because point data do not capture the spatial extent of the land change, it is difficult to accurately measure the area or volume of the change. This can result in inaccurate calculations of important land change metrics, such as deforestation rates or urbanisation rates.
4. Limited ability to perform spatial analyses: Point data does not provide enough spatial information to perform many advanced spatial analyses, such as spatial interpolation, network analysis, or spatial pattern analysis. This can limit the range of analyses that can be performed on the data and can lead to less robust and informative results.
To find out more about how we capture, digitise, analyse and catalog our land use data, you can get in contact with us at firstname.lastname@example.org. Our team will be more than happy to speak with you!