Arc_Mat

Arc_Mat: a Matlab-based spatial data analysis toolbox. This article presents an overview of Arc_Mat, a Matlab-based spatial data analysis software package whose source code has been placed in the public domain. An earlier version of the Arc_Mat toolbox was developed to extract map polygon and database information from ESRI shapefiles and provide high quality mapping in the Matlab software environment. We discuss revisions to the toolbox that: utilize enhanced computing and graphing capabilities of more recent versions of Matlab, restructure the toolbox with object-oriented programming features, and provide more comprehensive functions for spatial data analysis. The Arc_Mat toolbox functionality includes basic choropleth mapping; exploratory spatial data analysis that provides exploratory views of spatial data through various graphs, for example, histogram, Moran scatterplot, three-dimensional scatterplot, density distribution plot, and parallel coordinate plots; and more formal spatial data modeling that draws on the extensive Spatial Econometrics Toolbox functions. A brief review of the design aspects of the revised Arc_Mat is described, and we provide some illustrative examples that highlight representative uses of the toolbox. Finally, we discuss programming with and customizing the Arc_Mat toolbox functionalities.


References in zbMATH (referenced in 61 articles , 1 standard article )

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  1. Arbia, Giuseppe; Bee, Marco; Espa, Giuseppe; Santi, Flavio: Fitting spatial regressions to large datasets using unilateral approximations (2018)
  2. Ay, Jean-Sauveur; Ayouba, Kassoum; Le Gallo, Julie: Nonlinear impact estimation in spatial autoregressive models (2018)
  3. Debarsy, Nicolas; Dossougoin, Cyrille; Ertur, Cem; Gnabo, Jean-Yves: Measuring sovereign risk spillovers and assessing the role of transmission channels: a spatial econometrics approach (2018)
  4. Gómez-Rubio, Virgilio; Rue, Håvard: Markov chain Monte Carlo with the integrated nested Laplace approximation (2018)
  5. Hillier, Grant; Martellosio, Federico: Exact and higher-order properties of the MLE in spatial autoregressive models, with applications to inference (2018)
  6. Ho, Chun-Yu; Wang, Wei; Yu, Jihai: International knowledge spillover through trade: a time-varying spatial panel data approach (2018)
  7. Kang, Xiaojuan; Li, Tizheng: Testing a linear relationship in varying coefficient spatial autoregressive models (2018)
  8. Song, Malin; Peng, Jun; Wang, Jianlin; Zhao, Jiajia: Environmental efficiency and economic growth of China: a ray slack-based model analysis (2018)
  9. Song, Mengdi: Network effects of countries’ exchange rate regime choices: a spatial analysis (2018)
  10. Suesse, Thomas: Marginal maximum likelihood estimation of SAR models with missing data (2018)
  11. Xiaozhi, Peng; Hecheng, Wu; Ling, Ma: Asymptotic properties of the estimators of the semi-parametric spatial regression model (2018)
  12. Xu, Xingbai; Lee, Lung-fei: Sieve maximum likelihood estimation of the spatial autoregressive Tobit model (2018)
  13. Yang, Jing; Lu, Fang; Yang, Hu: Quantile regression for robust inference on varying coefficient partially nonlinear models (2018)
  14. Zhang, Xinyu; Yu, Jihai: Spatial weights matrix selection and model averaging for spatial autoregressive models (2018)
  15. Baltagi, Badi H.; Egger, Peter H.; Kesina, Michaela: Determinants of firm-level domestic sales and exports with spillovers: evidence from China (2017)
  16. Espa, Giuseppe; Giuliani, Diego; Santi, Flavio; Taufer, Emanuele: Model-based variance estimation in two-dimensional systematic sampling (2017)
  17. Liu, Jianmin; Chen, Xia; Wei, Runchu: Socioeconomic drivers of environmental pollution in China: a spatial econometric analysis (2017)
  18. Sato, Takaki; Matsuda, Yasumasa: Spatial autoregressive conditional heteroskedasticity models (2017)
  19. Yang, Kai; Lee, Lung-fei: Identification and QML estimation of multivariate and simultaneous equations spatial autoregressive models (2017)
  20. Bavaud, François: Testing spatial autocorrelation in weighted networks: the modes permutation test (2016)

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