Case11A-Measuring Wasteful Commuting in Columbus

Chapter 11 Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers

This chapter introduces two popular methods in optimization, linear program ming (LP) and quadratic programming (QP). QP is perhaps the simplest form of non-linear programming (NLP). LP and QP seek to maximize or minimize an objective function subject to a set of constraints. LP has both the objective and the constraints in linear functions. QP has a quadratic objective function, but its constraints remain linear. This chapter uses case studies to illustrate their applications in spatial planning and location-allocation analysis.

Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio

Case study 11A examines the issue of wasteful commuting in Columbus, Ohio to illustrate the formulation of LP and its solution.

Data used in this case study is based on a study reported in Wang (2001b). The data folder Columbus includes:

1. a zipped area feature class urbtaz.zip and its corresponding point feature class urbtazpt.zip with 991 TAZs (traffic analysis zones and their centroids, respectively)

2. a zipped feature dataset roads.zip containing a single feature class roads for the road network, and

3. R file WasteCommute.r for measuring waste commute.

Computational Methods and GIS Applications in Social Science - KNIME Lab Manual
Lingbo Liu, Fahui Wang

External resources

• Github for Geospatial Analytics
• Github for Workbook Issue Report