Spatial Analysis Overview

Examines how the spatial distribution of data points changes over time. Helps understand if points are more clustered or dispersed compared to a previous period.

A statistical method that investigates whether similar values in a dataset tend to cluster together or are dispersed. It helps determine if there's a pattern beyond random distribution.

A fundamental concept in geography stating that locations closer to each other tend to have more similar characteristics than those farther apart. It emphasizes the importance of proximity in spatial analysis.

A type of spatial autocorrelation where similar values are scattered throughout the study area, meaning nearby locations are likely to have different values. It suggests an underlying process that causes a regular dispersion pattern.

A type of spatial autocorrelation where similar values tend to cluster together, meaning nearby locations are likely to have similar values. It indicates a process leading to clustering or aggregation.

The study of geographic data to discover relationships and patterns within it. Think about how elements relate to each other – like income and access to public transit.

It's the practice of examining the positions of things in space to uncover tendencies in their location. This can be applied to any theme within geographic data. Think about the location of restaurants.

It's a point pattern where locations are evenly spread out across the area. Think of seating in a classroom.

It's a point pattern where locations are randomly distributed, with no clear grouping or spacing pattern. Think about raindrops.

It's a point pattern where locations are bunched together.  Think about a crowd gathering around a stage.

Spatial analysis can examine a singular factor or multiple factors that affect a location. We can start with a single variable and then add in more to expand our understanding. Think about how both population density and average income could influence restaurant location.

Spatial analysis can provide evidence for trends that we observe through visual means by quantifying those patterns. This allows us to confirm whether there’s a real relationship between elements or if it's just a coincidence. Think about how the temperature of a place and its income level might appear connected when looking at a map, but analysis can verify this through data.

Spatial analysis is a powerful tool that allows us to address questions about how things in space relate to each other. It can be used to explore topics like environmental hazards, access to resources, and social patterns, helping us understand how these things work together and impact each other. Think about how knowing where potholes are can help plan road repairs, or how understanding where people live near a factory can help protect their health.

Analysis focusing on the spatial relationships between different datasets or themes. It helps understand how elements interact in space, like burglaries and police stations.

A straight-line distance between two points, representing the shortest possible distance between them.

The distance you must travel using a transportation network to reach a destination, taking into account available routes and paths.