Book Review: Triumph of the City, Edward Glaeser

JTLU vol. 6, no.3, pp. 87-89 (2013)The authors review the book Triumph of the City by Edward Glaeser (Penguin Press, 2011)

Multi-dimensional geometric complexity in urban transportation systems

Transportation networks serve as windows into the complex world of urban systems. By properly characterizing a road network, one can better understand its encompassing urban system. This study offers a geometrical approach toward capturing inherent properties of urban road networks. It offers a robust and efficient methodology toward defining and extracting three relevant indicators of road networks—area, line, and point thresholds—through measures of their grid equivalents. By applying the methodology to 50 U.S. urban systems, one can successfully observe differences between eastern versus western, coastal versus inland, and old versus young cities. Moreover, we show that many socioeconomic characteristics, as well as travel patterns, within urban systems are directly correlated with their corresponding area, line, and point thresholds

GTdownloader: A Python Package to Download, Visualize, and Export Georeferenced Tweets From the Twitter API

This article describes GTdowloader, a Python package that serves as both an API wrapper and as a geographic information pre-processing helper to facilitate the download of Twitter data from the Twitter API. Specifically, the package offers functions that enable the download of Twitter data through single functions that integrate access to the API call parameters in the form of familiar Python functions syntax. In addition, the data is available for download in common formats for further analysis, which includes standard Geographic Information Systems (GIS) vector format. This software package is especially useful for users with little to no experience with building API calls but who would highly benefit from access to Twitter data

Causation versus Prediction: Comparing Causal Discovery and Inference with Artificial Neural Networks in Travel Mode Choice Modeling

This study compares the performance of a causal and a predictive model in modeling travel mode choice in three neighborhoods in Chicago. A causal discovery algorithm and a causal inference technique were used to extract the causal relationships in the mode choice decision making process and to estimate the quantitative causal effects between the variables both directly from observational data. The model results reveal that trip distance and vehicle ownership are the direct causes of mode choice in the three neighborhoods. Artificial neural network models were estimated to predict mode choice. Their accuracy was over 70%, and the SHAP values obtained measure the importance of each variable. We find that both the causal and predictive modeling approaches are useful for the purpose they serve. We also note that the study of mode choice behavior through causal modeling is mostly unexplored, yet it could transform our understanding of the mode choice behavior. Further research is needed to realize the full potential of these techniques in modeling mode choice