Overview of Contributions in<i>Geographical Analysis</i>: Waldo Tobler

Murray, Alan T.; Xu, Jing; Baik, Jiwon; Burtner, Susan; Cho, Seonga; Noi, Evgeny; Pludow, B. Amelia; Zhou, Enbo

doi:10.1111/gean.12257

Cited by 3 publications

(1 citation statement)

References 37 publications

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“…According to spatial autocorrelation and spatial non-stationarity properties for spatial data, it is less possible to use basic global regressions such as Ordinary Least square (Murray et al, 2020). In this model, the spatial dependencies between the events are considered as weight matrices, and due to the heterogeneity of the environmental factors and the existence of local variation, regression coefficients of the GWR model for observation are measured locally (Wu, 2020).…”

Section: Geographically Weighted Regressionmentioning

confidence: 99%

Development of a Data-Driven Model to Predict Landslide Sensitive Areas

Eslaminezhad¹,

Omarzadeh²,

Eftekhari³

et al. 2021

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The occurrence of landslides has always been a problem in spatial planning as one of the environmental threats. The aim of the present study is to estimate the landslide sensitive areas in the Urmia Lake basin based on determining effective criteria and spatial and non-spatial data-driven models. The criteria used in this research include distance to faults, distance to roads, distance to hydrology network, land use, lithology, soil classes, Elevation, slope, aspect and Precipitation. The novelty of this study is to present new combination approaches to determine the effective criteria in landslide sensitive areas (Urmia Lake basin). In this regard, the geographically weighted regression (GWR) with exponential and bi-square kernels and artificial neural network (ANN) combined with a binary particle swarm optimization algorithm (BPSO). The best value of the fitness function (1-R2) for ANN, GWR with the exponential kernel, and GWR with bi-square kernel was obtained 0.2780, 0.07453, and 0.0022, respectively, Which indicates higher compatibility of the bi-square kernel than the other models. It was also found that the criteria used have a significant effect on the landslide sensitive zoning.

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Section: Geographically Weighted Regressionmentioning

confidence: 99%

Development of a Data-Driven Model to Predict Landslide Sensitive Areas

Eslaminezhad¹,

Omarzadeh²,

Eftekhari³

et al. 2021

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Facility Layout and Spatial Configuration Efficiency Assessment

Zhou,

Murray

2024

Netw Spat Econ

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With rapid regional development and urbanization, many public and private facilities and infrastructures (e.g., sirens, cellphone base stations, bike sharing stations, wind turbines, etc.) require regular renovation or supplementation. Evaluating existing facility efficiency and expanding to new facility locations are of broad interest among stakeholders, including businesses, urban planners, government agencies, and the public more generally. Such evaluation can be used to improve overall social accessibility, equity and efficiency by reconfiguring or adding new facilities in the best way possible. A regularly distributed lattice is often viewed as an optimal configuration given important observed properties and characteristics. In this paper, we formulate a spatial optimization model to evaluate spatial coverage efficiency. Specifically, given two sets of points, the model seeks the optimal location and orientation of an idealized lattice to align with an existing facility configuration. The distance between existing facilities and the ideally configured lattice under the optimal alignment represents efficiency. An iterative heuristic based on gradient descent and spatial indexing is developed to solve this problem. Extensive computational experience demonstrates the importance of this problem and the effectiveness of the derived solution approach, as well as highlights assistance provided to decision makers in identifying inefficiencies as well as improving existing infrastructure service systems.

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