Table 2 A comparison table of GIS analysis tools employed in this study

Description

Evaluates spatial distribution by calculating distances between features and their nearest neighbors.

Creates zones or circular areas around geographic features to illustrate their spatial impact or influence.

Determines areas accessible from one or more service points within a network, considering factors like travel time and road networks.

Purpose

Analyze the spatial pattern of points (clustered, random, or dispersed)

Create zones of a specified distance around features (points, lines, or polygons)

Define areas accessible within a given time or travel cost from a set of facilities

Input data

Point data only

Any type of feature data (points, lines, polygons)

Point data representing facilities, network data for travel paths

Output data

Statistical measures of spatial pattern (e.g., nearest neighbor ratio, Ripley's K)

Polygons representing buffer zones around features

Polygons representing areas accessible within the specified time/cost

Applications

Identify areas with potential environmental hazards, assess disease clusters, analyze urban development patterns

Site selection for new facilities, determine service areas and operational ranges for existing facilities, based on predefined buffer distances, analyze accessibility to resources

Planning public transportation routes, identifying areas under served by emergency services, analyzing market potential

Visualization

Maps with points color-coded by nearest neighbor distances or Ripley's K values

Maps with buffer zones around features

Maps with areas color-coded by travel time/cost from facilities

Strengths

Provides insights into patterns within datasets.

Easily visualizes spatial influence of features.

Accounts for real-world travel constraints and provides realistic service coverage.

Limitations

Assumes uniform distribution of points in a random pattern, may not be suitable for clustered or dispersed patterns

Sensitive to scale and projection, buffer shapes may not accurately represent real-world accessibility

Relies on accurate network data and travel cost/time models, may not capture individual travel behavior