Understanding the Resource Intensity of Cities

A Systematic Approach to Interactive Visualization of the Urban Environment

March 3, 2012

Interactive Visualization and Measurement of Urban Areas

Urban planning has focused on identifying many important questions about the formation and functioning of our cities. However, there is a lack of understanding about the spatial patterns related to material and energy use in cities. This work attempts to address this knowledge gap.

urbmet.org is a web-map that illustrates data on material and energy use in cities. The goal is to provide an intuitive way of understanding this complex problem using an interactive interface. We have analyzed 42 cities and estimated material and energy intensities.

To make this work as useful as possible, we are interested in examining whether this information is presented in such a way that it builds intuition about the functioning of cities.

Relating patterns of urban form to resource intensities.

Data Sources and Analysis Methods

Building and Road Calculations

We have developed estimates of the road area and residential building area by building regression models from cities with complete datasets, and applying these predictive models to cities where we are missing data.

Material Conversions

We assembled survey data that described building construction methods for a variety of roads and buildings. We used these factors to convert our geometric measurements of the urban form into KG of construction material. There are some inherent assumptions in the analysis; the values for these assumptions can be viewed here, and you can write comments.

Vehicle Kilometers Travelled (VKT)

Using comprehensive empirical vehicle data from MAPC we related geometric measurements of the built environment to VKT values for the state of Massachusetts. We then applied this simple transportation model to other urban areas. This method used a combination of network measures, census data and proximity to services. We also checked our accuracy against Texas Transportation Institute data.

Building Energy Data

We are using third party estimates of residential zip-code level electricity and gas consumption. The methods of estimation are described in the following study. We are using this data courtesy of Efficiency 2.0.

Technical Details

The data displayed in the online tool can also be accessed via a Web Map Service (WMS) using the following links:

Material:http://urbmet.org/mapserv.cgi?map=material.map
Energy:http://urbmet.org/mapserv.cgi?map=energy.map
Population:http://urbmet.org/mapserv.cgi?map=population.map

To connect to the WMS any standard GIS software can be used. Two open-source examples are QGIS and OpenJump.

We also plan to make our data available using a Web Coverage Service (WCS). This service is not yet available; please contact us if you are interested in using it.

We strive to use open-source tools in our analysis process, and in the functioning of this website. While commercial software was used for small parts of the analysis, the web tool is entirely based on open-source software:

Javascript:Openlayers, JQuery, Raphael, HighCharts
Database: PostGreSQL, PostGIS 2.0
Map Server:MapServer 6.0
Contact

This site was developed by David Quinn and Daniel Wiesmann. Our PhD advisors are John E Fernandez (MIT) and Paulo Ferrão (IST). This is our first large web-mapping project. If you are interested in our previous work follow this link: http://projects.urbmet.org

We welcome critiques of our work and we are interested in suggestions on how to improve our analysis. If you have comments or doubts about our work, please send us an email or leave a comment at our blog.

We have analyzed 42 cities so far, if you are interested in the inclusion of your city you are invited to fill out the following form. At the moment we cannot promise to include it, but we will consider all requests.

If the web tool did not display properly on your screen please give us feedback about your system and we will try to address the problem.

The Neighborhood Visualizer is a collaborative project between David Quinn and Daniel Wiesmann. Our PhD advisors are John E Fernandez (MIT) and Paulo Ferrão (IST).

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

Thanks to Mike Flaxman, Chris Zegras and Les Norford for their valuable suggestions about this work.

Special thanks to collaborator Juan Jose Sarralde for his advice and help.