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.

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  1. Yang, Kai; Lee, Lung-fei: Identification and QML estimation of multivariate and simultaneous equations spatial autoregressive models (2017)
  2. Blasques, Francisco; Koopman, Siem Jan; Lucas, Andre; Schaumburg, Julia: Spillover dynamics for systemic risk measurement using spatial financial time series models (2016)
  3. Dai, Xiaowen; Jin, Libin; Shi, Anqi; Shi, Lei: Outlier detection and accommodation in general spatial models (2016)
  4. Glass, Anthony J.; Kenjegalieva, Karligash; Sickles, Robin C.: A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers (2016)
  5. Pal, Amresh Bahadur; Dubey, Ashutosh Kumar; Chaturvedi, Anoop: Shrinkage estimation in spatial autoregressive model (2016)
  6. Sun, Yiguo: Functional-coefficient spatial autoregressive models with nonparametric spatial weights (2016)
  7. Yang, Jing; Yang, Hu: Smooth-threshold estimating equations for varying coefficient partially nonlinear models based on orthogonality-projection method (2016)
  8. Debarsy, Nicolas; Jin, Fei; Lee, Lung-fei: Large sample properties of the matrix exponential spatial specification with an application to FDI (2015)
  9. Delgado, Miguel A.; Robinson, Peter M.: Non-nested testing of spatial correlation (2015)
  10. Goryainov, V.B.; Goryainova, E.R.: Asymptotic properties of the sign estimate of autoregression field coefficients (2015)
  11. Griffith, Daniel A.; Chun, Yongwan: Spatial autocorrelation in spatial interactions models: geographic scale and resolution implications for network resilience and vulnerability (2015)
  12. Su, Liangjun; Yang, Zhenlin: QML estimation of dynamic panel data models with spatial errors (2015)
  13. Wang, Wei; Yu, Jihai: Estimation of spatial panel data models with time varying spatial weights matrices (2015)
  14. Xu, Xingbai; Lee, Lung-fei: Maximum likelihood estimation of a spatial autoregressive Tobit model (2015)
  15. Xu, Xingbai; Lee, Lung-Fei: A spatial autoregressive model with a nonlinear transformation of the dependent variable (2015)
  16. Yang, Fuxia; Yang, Mian: Analysis on China’s eco-innovations: regulation context, intertemporal change and regional differences (2015)
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  19. Młodak, Andrzej: Neighbourhood of spatial areas in the physical and socio-economical context (2013)
  20. Wu, Billy; Yao, Qiwei; Zhu, Shiwu: Estimation in the presence of many nuisance parameters: composite likelihood and plug-in likelihood (2013)

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