Geospatial Methods

OPGD Model and “GD” R Package for Spatial Factor Exploration

Optimal Parameters-based Geographical Detectors (OPGD) model is used to characterising spatial heterogeneity, identify geographical factors, and estimating risks.

Related Publications: Song et al., 2020. The R package “GD” supports the OPGD modelling.

IDSA: Interactive Detectors for Spatial Associations

Interactive Detector for Spatial Associations (IDSA) model is used to estimate power of interactive determinants (PID) from a spatial perspective. The IDSA model considers spatial heterogeneity, spatial autocorrelation, and spatial fuzzy overlay of multiple explanatory variables for calculating PID.

Related Publications: Song et al., 2021, IJGIS. The R package “IDSA” supports the IDSA modelling.

HS: Homogeneous Segmentation

Spatial Heterogeneity-based Segmentation (SHS) model is used to segment spatial line data. The SHS model is implemented in redefining road segments with high-resolution sensor monitoring data.

Related Publications: Song et al., 2020, IEEE-ITS. The R package “HS” is used for SHS modelling.

Spatial Big Data Method for Redefining Cities

Nature cities or central urban regions can be identified using a spatial big data-driven method. The commonly used data include point of interests (POI) and social media data.

Related Publications: Song et al., 2018 IJGIS.

Line Segment Regression Kriging

Segment-based Regression Kriging (SRK) is used for spatial interpolation and prediction of line segment data, such as road and traffic data. Kriging covariance functions is estimated based on the covariance between any two line segments.

Related Publications: Song et al., 2019, IEEE-ITS. (ESI Highly Cited Paper) The SRK model can be achieved by “SK” R package.

MKD2SFCA: Spatiotemporal Accessibility

Travel time-based modified kernel density two-step floating catchment area (MKD2SFCA) model is used to compute population accessibility to public facilities, such as hospitals.

Related Publications: Song et al., 2018, GRS. (ESI Highly Cited Paper)

MFSD: Spatial Decision Making

Model-driven fuzzy spatial multi-criteria decision making (MFSD) approach is used to generate data and model driven sustainable road infrastructure performance indicators and reduce potential biases of human decisions..

Related Publications: Song et al., 2021, RSER.

Spatial Disparities of Road Impacts

Spatial heterogeneity model is used to examine spatial disparities in the relationship between road impacts on the economy and impacts on the roadside environment.

Related Publications: Luo, P., Song, Y.*, et al., 2021, GRS.

EOSI: Earth Data for Sustainable Infrastructure

Earth observation has great potentials for sustainable infrastructure development. EOSI benefits about 85% of infrastructure influenced SDGs and 61% of all 169 SDG targets, but Earth observation is only implemented in 15% of infrastructure influenced SDG targets, and 70% of infrastructure influenced targets that can be directly or indirectly derived from Earth observation data have not been included in current SDG indicators.

Related Publications: Song et al., 2021, RS.

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Email: yongze.song@curtin.edu.au

School of Design and the Built Environment,
Curtin University

Kent Street, Bentley, 6102, WA, Australia