GIS Spatial Statistics 2

Question 1

Kernel Density Estimation (KDE) in GIS is primarily used to:

Accepted Answer

Create a smooth continuous surface estimating the density of features

Answer

KDE places a kernel function over each point and sums the contributions to create a smooth density surface showing where features concentrate.

Question 2

Local Indicators of Spatial Association (LISA) are designed to:

Accepted Answer

Identify local spatial clusters and spatial outliers at each feature location

Answer

LISA statistics decompose global measures like Moran's I into location-specific values, identifying local clusters and outliers rather than a single summary statistic.

Question 3

In a Complete Spatial Randomness (CSR) test for point patterns, the null hypothesis states that:

Accepted Answer

Point locations are distributed randomly with no spatial dependence

Answer

The CSR null hypothesis assumes points are generated by a homogeneous Poisson process, meaning each location is equally likely to contain a point independently of other points.

Question 4

The Anselin Local Moran's I classifies locations into which output categories?

Accepted Answer

High-High, Low-Low, High-Low, and Low-High spatial association types

Answer

Local Moran's I produces four cluster/outlier types: High-High and Low-Low (spatial clusters) and High-Low and Low-High (spatial outliers).

Question 5

Ripley's K function is used in spatial statistics to:

Accepted Answer

Analyze point pattern clustering or dispersion across multiple distance scales

Answer

Ripley's K function counts the number of neighboring points within increasing distances, allowing analysis of clustering or dispersion at multiple spatial scales.

GIS - Geographic Information System Practice Test

GIS - Geographic Information System Practice Test

GIS Spatial Statistics 2