Kernel density estimation (KDE) is regarded as the most suitable ______________for visualizing crime data.

Question

Quizplus · Accepted Answer

Kernel density estimation (KDE) is a spatial analysis technique that creates a continuous surface from point data. It is commonly used for visualizing crime data as it can highlight where crime hotspots are occurring and provide insights into crime patterns and trends. Bar graphs and tables or figures can be useful for presenting aggregated crime data, but they do not provide the same level of spatial understanding that KDE can offer. Simple maps can also be useful, but they may not effectively capture the distribution and intensity of crime in a given area.

Kernel Density Estimation (KDE) Is Regarded as the Most Suitable