## What is local spatial analysis?

Local measures of spatial association are statistics used to detect variations of a variable of interest across space when the spatial relationship of the variable is not constant across the study region, known as spatial non-stationarity or spatial heterogeneity.

**What is spatial autocorrelation analysis?**

Spatial autocorrelation analysis consists of a set of statistics describing how a variable (e.g., crime likelihood) is autocorrelated through space (Hardy & Vekemans, 1999). This variable can also be an attribute of street segments.

### What is the difference between a global and local Moran’s I analysis?

The Local Moran’s I statistic is relatively similar to the Global Moran’s I in that it is providing a measure of how similar locations are to their neighbours. However, the difference is that each location, i, receive its own I value, as well as its own variance, z value, expected I, and variance of I.

**What does Local Moran’s I show?**

Local Moran’s I is a local spatial autocorrelation statistic that identifies local clusters or local outliers to understand their contribution to the ‘global’ clustering statistic. It was developed by Anselin (1995) as a class of local indicators called Local Indicators of Spatial Association (LISAs).

#### What is local spatial autocorrelation?

Local measures of spatial autocorrelation focus on the relationships between each observation and its surroundings, rather than providing a single summary of these relationships across the map. In this sense, they are not summary statistics but scores that allow us to learn more about the spatial structure in our data.

**What is a Moran scatterplot?**

The Moran scatterplot is an illustration of the relationship between the values of the chosen attribute at each location and the average value of the same attribute at neighboring locations.

## What is an example of spatial autocorrelation?

Positive Spatial Autocorrelation Example Positive spatial autocorrelation occurs when Moran’s I is close to +1. This means values cluster together. For example, elevation datasets have similar elevation values close to each other. There is clustering in the land cover image above.

**How does the GI * statistic differ from the Moran’s I clustering tool?**

Moran’s I can be expressed in terms of the local Gi* values. This is outlined in Section 4 of the 1995 paper of Ord & Getis and it shows that both measures are related to each other. While Moran’s I gives you a more general indication of clustering and repulsion, Gi* is a measure of high/low value concentration.

### How do you read Local Moran’s I?

Interpretation. A positive value for I indicates that a feature has neighboring features with similarly high or low attribute values; this feature is part of a cluster. A negative value for I indicates that a feature has neighboring features with dissimilar values; this feature is an outlier.

**How do you read local Moran?**

#### What is Lisa analysis?

LISA Principle A LISA is seen as having two important characteristics. First, it provides a statistic for each location with an assessment of significance. Second, it establishes a proportional relationship between the sum of the local statistics and a corresponding global statistic.

**What is spatial autocorrelation used for?**

The importance of spatial autocorrelation is that it helps to define how important spatial characteristic is in affecting a given object in space and if there is a clear relationship of objects with spatial properties.

## How do I carry out a spatial autocorrelation analysis?

To carry out the spatial autocorrelation analysis, we will need a spatial weights file, either created from scratch, or loaded from a previous analysis (ideally, contained in a project file). The Weights Manager should have at least one spatial weights file included, e.g., guerry_85_q for first order queen contiguity.

**What is the local form of autocorrelation?**

Most global spatial autocorrelation statistics can be expressed as a double sum over the i and j indices, such as ∑ i ∑ j g i j. The local form of such a statistic would then be, for each observation (location) i, the sum of the relevant expression over the j index, ∑ j g i j.

### Is there a local Moran statistic for spatial autocorrelation?

As there are many statistics for global spatial autocorrelation, there will be many corresponding LISA. In this Chapter, we focus on the local counterpart of Moran’s I. The Local Moran statistic was suggested in Anselin ( 1995) as a way to identify local clusters and local spatial outliers.

**What does it mean when autocorrelation is negative?**

To review, spatial autocorrelation measures the correlation of a variable with itself across space. Positive spatial autocorrelation means that the locations close together have similar values, while negative spatial autocorrelation means that locations close together have more dissimilar values than those locations further away.