A Synthesis of Spatial Models for Multivariate Count Responses

Yiyi Wang (), Kara Kockelman and Amir Jamali
Additional contact information
Yiyi Wang: Montana State University
Kara Kockelman: University of Texas at Austin
Amir Jamali: Montana State University

Chapter Chapter 14 in Regional Research Frontiers - Vol. 2, 2017, pp 221-237 from Springer

Abstract: Abstract This chapter provides a synthesis of spatial data mining models for analyzing multivariate count responses. Geo-referenced multivariate count responses are common in regional science (e.g., registered vehicle counts by body type and firm/job counts by industry type), but are computationally difficult to model—especially when sample size is large. This chapter synthesizes relevant research and offers lessons learned and best practices for future research.

Keywords: Spatial econometric model; Spatial autocorrelation; Multivariate response; Bayesian estimation (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:adspcp:978-3-319-50590-9_14

Ordering information: This item can be ordered from
http://www.springer.com/9783319505909

DOI: 10.1007/978-3-319-50590-9_14

Access Statistics for this chapter

More chapters in Advances in Spatial Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().