Dynamic nearest neighbours for generating spatial weight matrix

Mawarni, Mutiara; Machdi, Imam

doi:10.1109/icacsis.2016.7872771

Cited by 3 publications

(6 citation statements)

References 3 publications

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“…For the spatial econometric model, different spatial weight matrices will eventually produce different results [ 81 ]. Therefore, different spatial weight matrices are considered for robustness testing [ 16 ].…”

Section: Resultsmentioning

confidence: 99%

Impacts of Cross-Sectoral Climate Policy on Forest Carbon Sinks and Their Spatial Spillover: Evidence from Chinese Provincial Panel Data

Zhu

Cai

Lin

et al. 2022

IJERPH

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This paper examines the impact of cross-sectoral climate policy on forest carbon sinks. Due to the complexity of the climate change issue and the professional division of labor among government departments, cross-sectoral cooperation in formulating climate policy is a desirable strategy. Forest carbon sinks play an important role in addressing climate change, but there are few studies focusing on forest carbon sinks and cross-sectoral climate policies. Thus, based on the panel data of 30 provinces and cities in China from 2007 to 2020, this paper establishes a benchmark regression model and a spatial panel model to analyze the impact of cross-sectoral climate policies on forest carbon sinks. We find that cross-sectoral climate policies positively impact forest carbon sinks. Under the influence of the “demonstration effect”, we find that cross-sectoral climate policies have a positive impact not only on the forest carbon sinks in the region but also on those in the neighboring region. Further analysis shows that for provinces with less developed forestry industry and small forest areas, the positive effect of cross-sectoral climate policies on forest carbon sinks is more obvious. Overall, this paper can serve as an important reference for local governments to formulate climate policies and increase the capacity of forest carbon sinks.

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Section: Resultsmentioning

confidence: 99%

Impacts of Cross-Sectoral Climate Policy on Forest Carbon Sinks and Their Spatial Spillover: Evidence from Chinese Provincial Panel Data

Zhu

Cai

Lin

et al. 2022

IJERPH

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“…Spatiotemporal panel data analysis relies on accurately defining the spatial relationships between features. One crucial aspect of spatiotemporal analysis is determining the most suitable SWM to accurately analyze the data (Mawarni & Machdi, 2016). There are two main methods for generating SWM: distance-band and contiguity-based.…”

Section: Spatial-temporal Weight Matrix (Stwm)mentioning

confidence: 99%

“…While various SWMs have been proposed, many studies (Getis & Aldstadt, 2004; Mawarni & Machdi, 2016) rely solely on exogenous geographical factors, such as proximity or distance between samples. However, this approach neglects the influence of time‐varying economic variables that are often endogenous to the spatial system.…”

Section: Introductionmentioning

confidence: 99%

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

Mohammadi,

Taleai,

Otto

et al. 2024

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Contemporary spatial statistics studies often underestimate the complexity of road networks, thereby inhibiting the strategic development of effective interventions for car accidents. In response to this limitation, the primary objective of this study is to enhance the spatiotemporal analysis of urban crash data. We introduce an innovative spatial‐temporal weight matrix (STWM) for this purpose. The STWM integrates external covariates, including road network topological measurements and economic variables, offering a more comprehensive view of the spatiotemporal dependence of road accidents. To evaluate the functionality of the presented STWM, random effect eigenvector spatial filtering analysis is employed on Boston's traffic accident data from January to March 2016. The STWM improves analysis, surpassing distance‐based SWM with a lower residual standard error of 0.209 and a higher adjusted R2 of 0.417. Furthermore, the study emphasizes the influence of road length on crash incidents, spatially and temporally, with random standard errors of 0.002 for spatial effects and 0.026 for non‐spatial effects. This is particularly evident in the north and center of the study area during specific periods. This information can help decision‐makers develop more effective urban development models and reduce future crash risks.

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“…As taking linear roads as the research object in this paper, we need to build the SWMR that matches with spatial distribution characteristics of roads for identification of RCHC by using cluster and outlier analysis. Conceptually, the SWMR is an N×N matrix (as shown in Equation (1) [26,27]. There is one row for every road and one column for every road.…”

Section: Swmr Construction Algorithmmentioning

confidence: 99%

“…Crashes are spatially aggregated as the attribute of the count of crashes of road (ACCR) by considering the road-crash geometric and attribute relationships. Then, a spatial weight matrix of roads (SWMR) [26][27][28] is established based on the road-road topological and geometric relationships. The ACCRs and SWMR are used as the input parameters in the cluster and outlier analysis (local Moran's I) to improve accuracy of the result of spatial data mining.…”

Section: Introductionmentioning

confidence: 99%

Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach

2019

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This paper develops a three-step spatial data mining approach to directly identify road clusters with high-frequency crashes (RCHC). The first step, preprocessing, is to store the roads and crashes in a spatial database. The second step is to describe the conceptualization of road–road and crash–road spatial relationships. The spatial weight matrix of roads (SWMR) is constructed to describe the conceptualization of road–road spatial relationships. The conceptualization of crash–road spatial relationships is established using crash spatial aggregation algorithm. The third step, spatial data mining, is to identify RCHC using the cluster and outlier analysis (local Moran’s I index). This approach was validated using spatial data set including roads and road-related crashes (2008–2018) from Polk County, IOWA, U.S.A. The findings of this research show that the proposed approach is successful in identifying RCHC and road outliers.

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Dynamic nearest neighbours for generating spatial weight matrix

Cited by 3 publications

References 3 publications

Impacts of Cross-Sectoral Climate Policy on Forest Carbon Sinks and Their Spatial Spillover: Evidence from Chinese Provincial Panel Data

Impacts of Cross-Sectoral Climate Policy on Forest Carbon Sinks and Their Spatial Spillover: Evidence from Chinese Provincial Panel Data

Analyzing urban crash incidents: An advanced endogenous approach using spatiotemporal weights matrix

Identify Road Clusters with High-Frequency Crashes Using Spatial Data Mining Approach

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