Layered Maps

Introduction

In GIS data visualization, one technique that stands out as particularly powerful is the ability to overlay, or superpose, multiple layers of information to form a composite map.

This method allows us to synthesize diverse spatial data, consequently, deriving new insights and painting a comprehensive picture of the subject at hand. For example, by overlaying a road map, a population density layer, and a layer of public amenities, we could gain a nuanced understanding of urban planning challenges and opportunities.

The strength of GIS, and by extension GeoPandas, lies in its inherent ability to manage the integration of diverse spatial data seamlessly. Drawing parallels to the traditional art of cartography where layers of ink are meticulously added to a canvas, in GeoPandas we progressively append each layer to the same axis. This process effectively overlays fresh information onto the existing data layer.

A crucial aspect of this technique is the precise spatial alignment of these layers, irrespective of their individual underlying projections. This precision is made possible by the CRS property. It serves as a backbone, ensuring that all spatial data— despite their potential variance in origin or scale—coexist harmoniously on a single map. This alignment is the foundation of spatial analysis, enabling us to juxtapose, compare, and integrate diverse datasets to gain multifaceted insights.

The order of the layers in a map composition is usually called z-order, in reference to third z-axes besides longitude (x-axis) and latitude (y-axis). The following figure shows an example of a multilayered composition for the rain anomaly in the São Francisco basin, in Brazil.