Speakers
Description
Due to the proliferation of big data and open data, cartography and spatial analysis have profoundly transformed in recent decades. These advancements have redefined how geographic information is collected, processed, and represented, enabling more dynamic, precise, and inclusive mapping practices. By leveraging open data repositories and big data analytics, cartographers can create maps that are accurate, predictive, and adaptive to changing circumstances.
Urban studies have particularly benefited from the availability of extensive datasets at various scales. Establishing connections between diverse data types is essential to comprehensively understanding and describing urban environments. This data is a critical foundation for formulating policies, designing projects, envisioning future scenarios, and developing local, national, and transnational strategic frameworks.
Developing representations that effectively support public policy-making and local projects requires integrating diverse data types, including social, demographic, and physical datasets. However, these datasets often rely on distinct geometric frameworks that are not inherently compatible. Consequently, establishing meaningful relationships among different datasets is a complex task that demands a thorough understanding of the specific data requirements and characteristics.
One of the most essential methodological approaches commonly observed in urban studies is a quantitative approach centered on data visualization, which may lack a direct connection to the physical territory. Alternatively, this approach often relates to the territory only through representations constrained by administrative boundaries. Such frameworks, however, need to be more frequently used to accurately describe the complex relationships between datasets and their spatial context. This study seeks a method to efficiently integrate the different geometries of data supports and develop more comprehensive and effective representations for urban studies for supporting projects and policies.
We defined a method to transfer data from a geometric support to a different one. This method is based on realizing a mathematical continuous model as an intermediate stage. The continuous model is the key to transferring data through different supports. Starting from the other geometry of the basis, different algorithms exist to transform data on different geometry (point, area, macro area, raster, etc.) in a continuous distribution of data, a shade of data that interpolates with the predictable original data. The data in the continuous model can be transferred to the final support that collects all the data that is necessary to relate. In this first phase, we decided to work on a few data sets based on different geometry: Data elevation model, school locations, population density, and land use. We chose to define a hexagonal grid as the final support because it is homogeneous in all the extensions of the territory we consider.
The first result of our work is the definition of the algorithms necessary to model data from every kind of geometric support to a continuous model based on a predictor that interprets the shape of territory. The second result is the distribution of some champion data from the original geometry to a standard grid. The third result is a clustering sample based on the first data analyzed.
To describe the territory, it is necessary to relate different finds and shapes of information.
We are sure that relating territorial data to space requires defining a standard geometry where it is possible to superpose the different data for portions of space. If the final geometry is homogeneous, the clustering obtained is a sample of the potential use of this methodology in urban studies to make clusters of different territories based on the homogeneous subdivision of the space.
With this method, every specific field of territory investigation is related to another. This makes it possible to draw a cartographic representation of the territory that is always measurable.
References
Bertain J. (1967), Sémiologie graphique. Les diagrammes - Les reseax – Les cartes, Paris: Éditions Gauthier-Villars.
Cressie, N. (1993). Statistics for Spatial Data (Revised Edition). New York: John Wiley & Sons.
Floridi, L. (2010), Information: A Very Short Introduction. Oxford: Oxford University Press, USA.
Wood, D. (2010), Rethinking the power of maps, New York: Guilford Press.
Pebesma, E.; Bivand, R. (2023). Spatial Data Science: With Applications in R (1st ed.). Boca Raton: Chapman and Hall/CRC.
| Keywords | Mapping, Big Data, Grid, Models |
|---|---|
| Best Congress Paper Award | No |
