Most data and measurements can be linked to specific locations and hence plotted on a map. You can tell what is present and where it is by using spatial data. The real world can be represented as discrete data, which is stored by its exact geographic location (referred to as “feature data”), or as continuous data, which is represented by regular grids (referred to as “raster data”). The nature of what you’re evaluating, of course, determines how it’s best portrayed. The natural environment (height, temperature, precipitation) is frequently represented with raster grids, but the constructed environment (roads, buildings) and administrative data (counties, census regions) are typically represented with vector data. Additional information describing what is present at each location can be connected; this information is commonly referred to as “attributes.”
Within the realm of Geographic Information Systems (GIS), datasets are managed as individual layers, each representing a specific set of spatial information. These layers can be combined and analyzed using a range of analytical operators, a process commonly known as overlay analysis. By leveraging these capabilities, GIS empowers users to work with multiple layers simultaneously, enabling exploration and discovery of crucial insights.
Spatial Analysis
GIS analysis can be used to answer questions like: Where’s the most suitable place for a housing development? A handful of seemingly unrelated factors—land cover, relative slope, distance to existing roads and streams, and soil composition—can each be modeled as layers, and then analyzed together using weighted overlay, a technique often credited to landscape architect Ian McHarg.
The true power of GIS lies in the ability to perform analysis. Spatial analysis is a process in which you model problems geographically, derive results by computer processing, and then explore and examine those results. This type of analysis has proven to be highly effective for evaluating the geographic suitability of certain locations for specific purposes, estimating and predicting outcomes, interpreting and understanding change, detecting important patterns hidden in your information, and much more. The primary objective is to develop a spatial problem-solving mindset and effectively utilize GIS tools to address spatial challenges.
Spatial analysis involves several essential workflows that constitute its foundation:
Spatial Data Exploration
Spatial data exploration involves interacting with a collection of data and maps related to answering a specific question, which enables you to then visualize and explore geographic information and analytical results that pertain to the question. This allows you to extract knowledge and insights from the data. Spatial data exploration involves working with interactive maps and related tables, charts, graphs, and multimedia. This integrates the geographic perspective with statistical information in the attributes. It’s an iterative process of interactive exploration and visualization of maps and data.
Modeling with GIS Tools:
GIS provides a wide range of tools and functions specifically designed for spatial analysis. These tools allow you to manipulate, combine, and analyze spatial data layers to derive meaningful results. Modeling with GIS tools allows one to simulate and understand complex spatial phenomena, explore alternative scenarios, and support decision-making processes in various fields such as urban planning, environmental management, transportation, and public health. By leveraging GIS capabilities, you can gain a deeper understanding of the relationships between spatial variables and make more informed and effective spatial decisions. Examples of common GIS tools for spatial analysis include buffering, spatial querying, interpolation, network analysis, and overlay operations.
Spatial analysis encompasses a wide range of techniques and methods that can be applied to explore and derive insights from spatial data. Some common types of spatial analysis include hotspot analysis, point-in-polygon analysis, spatial interpolation, network analysis, and spatial clustering, among others. These techniques provide valuable tools for identifying patterns, assessing relationships, and understanding the spatial characteristics of data. In upcoming blogs, we will delve into each of these types of spatial analysis, exploring their concepts, methodologies, and practical applications. Stay tuned to learn more about how these techniques can enhance your understanding of spatial data and support informed decision-making.
