Spnaf: An R package for analyzing and mapping the hotspots of flow datasets

Author:

Ha Hui Jeong1,Lee Youngbin2,Kim Kyusik3ORCID,Park Sohyun4,Lee Jinhyung1ORCID

Affiliation:

1. Western University, Canada

2. Seoul National University, South Korea

3. Florida State University, USA

4. George Mason University Korea, South Korea

Abstract

This paper introduces {spnaf} (spatial network autocorrelation for flows), an R package designed for the hotspot analysis of flow (e.g., human mobility, transportation, and animal movement) datasets based on Berglund and Karlström’s G index. We demonstrate the utility of the {spnaf} package through two example analyses by data forms: 1) bike-sharing trip patterns in Columbus, Ohio, USA, using polygon data, and 2) U.S. airports’ passenger travel patterns, using point data. The {spnaf} is available for download from the Comprehensive R Archive Network (CRAN), which contains a vignette and sample data/code for immediate use. This package addresses limitations in existing spatial analysis packages and emphasizes its efficiency in detecting flow hotspots. It is highly applicable in various urban and geographic data science applications. {spnaf} is still in its early stages and we hope that interested readers can contribute to the development and enhancement of the package.

Funder

Faculty of Social Science, Western University

National Research Foundation of Korea (NRF) grant funded by the Korea government

Publisher

SAGE Publications

Reference12 articles.

1. GeoDa, From the Desktop to an Ecosystem for Exploring Spatial Data

2. Identifying local spatial association in flow data

3. Bivand R, Bernat A, Carvalho M, et al. (2005) The spdep package. Available at: https://cran.r-project.org/web/packages/spdep/index.html

4. Modeling network autocorrelation within migration flows by eigenvector spatial filtering

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