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 88 articles , 1 standard article )

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  1. Glass, Anthony J.; Kenjegalieva, Karligash; Douch, Mustapha: Uncovering spatial productivity centers using asymmetric bidirectional spillovers (2020)
  2. Glass, Anthony J.; Kenjegalieva, Karligash; Weyman-Jones, Thomas: The effect of monetary policy on bank competition using the Boone index (2020)
  3. Glass, Anthony J.; Kenjegaliev, Amangeldi; Kenjegalieva, Karligash: Spatial scale and product mix economies in U.S. banking with simultaneous spillover regimes (2020)
  4. Hoshino, Tadao: Semiparametric estimation of censored spatial autoregressive models (2020)
  5. Hou, Zhezhi; Jin, Man; Kumbhakar, Subal C.: Productivity spillovers and human capital: a semiparametric varying coefficient approach (2020)
  6. Huang, Danyang; Wang, Feifei; Zhu, Xuening; Wang, Hansheng: Two-mode network autoregressive model for large-scale networks (2020)
  7. Jeong, Hanbat; Lee, Lung-fei: Spatial dynamic models with intertemporal optimization: specification and estimation (2020)
  8. Kutlu, Levent; Tran, Kien C.; Tsionas, Mike G.: A spatial stochastic frontier model with endogenous frontier and environmental variables (2020)
  9. Ma, Yingying; Lan, Wei; Zhou, Fanying; Wang, Hansheng: Approximate least squares estimation for spatial autoregressive models with covariates (2020)
  10. Murakami, Daisuke; Griffith, Daniel A.: A memory-free spatial additive mixed modeling for big spatial data (2020)
  11. Skevas, Ioannis: Inference in the spatial autoregressive efficiency model with an application to Dutch dairy farms (2020)
  12. Ushchev, Philip; Zenou, Yves: Social norms in networks (2020)
  13. Zhu, Xuening; Huang, Danyang; Pan, Rui; Wang, Hansheng: Multivariate spatial autoregressive model for large scale social networks (2020)
  14. Zhu, Yanli; Han, Xiaoyi; Chen, Ying: Bayesian estimation and model selection of threshold spatial Durbin model (2020)
  15. Bera, Anil K.; Doğan, Osman; Taşpınar, Süleyman: Testing spatial dependence in spatial models with endogenous weights matrices (2019)
  16. Dai, Xiaowen; Jin, Libin; Tian, Maozai; Shi, Lei: Bayesian local influence for spatial autoregressive models with heteroscedasticity (2019)
  17. de la Llave, Miguel Ángel; López, Fernando A.; Angulo, Ana: The impact of geographical factors on churn prediction: an application to an insurance company in Madrid’s urban area (2019)
  18. Furková, Andrea: Spatial spillovers and European union regional innovation activities (2019)
  19. LeSage, James P.; Chih, Yao-Yu; Vance, Colin: Markov chain Monte Carlo estimation of spatial dynamic panel models for large samples (2019)
  20. Li, Miaoqi; Kang, Emily L.: Randomized algorithms of maximum likelihood estimation with spatial autoregressive models for large-scale networks (2019)

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