Office of Research, UC Riverside
Sergio Rey
Professor of Public Policy
SPP - Dean's Office
serger@ucr.edu
(951) 827-1258


New Approaches for Spatial Distribution Dynamics

AWARD NUMBER
009318-002
FUND NUMBER
33394
STATUS
Active
AWARD TYPE
3-Grant
AWARD EXECUTION DATE
11/7/2017
BEGIN DATE
7/31/2017
END DATE
8/31/2018
AWARD AMOUNT
$73,967

Sponsor Information

SPONSOR AWARD NUMBER
1801801
SPONSOR
NATIONAL SCIENCE FOUNDATION
SPONSOR TYPE
Federal
FUNCTION
Organized Research
PROGRAM NAME

Proposal Information

PROPOSAL NUMBER
18040458
PROPOSAL TYPE
New
ACTIVITY TYPE
Basic Research

PI Information

PI
Rey, Sergio
PI TITLE
Other
PI DEPTARTMENT
SPP - Dean's Office
PI COLLEGE/SCHOOL
School of Public Policy
CO PIs

Project Information

ABSTRACT

This research project will improve understanding of spatial inequality dynamics through methodological advances in measurement and modeling. Understanding the nature of spatial inequality dynamics is vital to both basic social science and to public policy, yet existing methods and models provide incomplete views of these spatial dynamics. While a central focus of inequality research has been on the evolution of the aggregate income distribution, much less attention has been directed at the spatial pattern of inequality and pattern dynamics. Spatial inequalities can have important implications for social cohesion, economic growth, and the design of policies targeted at reducing the level of inequality. The advances produced in the project will have wide applicability. In addition to spatial income inequality dynamics, many other social and economic phenomena have distributions that evolve in space and time. Software packages will be delivered as open-source projects and accompanied with extensive tutorials and documentation to facilitate broad dissemination across the social sciences.

This research project will develop new analytical methods for the study of spatial dynamics of income inequality; specifically, new approaches will be developed to measuring changes in the distributional characteristics of those dynamics that incorporate their spatial dependence and heterogeneity. The new approaches will include both global measures that report summary properties of the spatial dynamics as well as local indicators that can be used to identify hot-spots of locations that are important drivers of the overall dynamics or are outliers from the global trends. Analytical and simulation based evaluations of the statistical properties of the new measures will be conducted, and empirical applications involving regional income inequalities will be carried out. These new analytics will be incorporated into enhanced versions of two open-source spatial analysis packages: Python Spatial Analysis Library and Space-Time Analysis of Regional Systems. The former provides social scientists who wish to develop custom applications with access to a modular library that can be used in conjunction with existing software to enable the new space-time analytics. The latter is a user-friendly analytical and visualization package that can facilitate exploratory investigation of spatial distribution dynamics.
(Abstract from NSF)