by Soledad Hernandez & Pinar Dursun


The millions of tons of waste disposed of into our environment every year. As urban growth continues to take hold in many cities, our levels of all types of waste, combined with the problems created when it comes to disposing of them, are constantly increasing. In front of this situation, an efficient management waste system can solve a basic problem in the cities.

Chicago generates 7,299,174 tons of waste every year and residents recycle just more than 200,000 tons of materials per year.

Chicago has two recycling systems: Blue Cart and Drop-off

The goals

The aim of this project is to analysis how is the recycling system en Chicago

Examining the effectiveness of Chicago Recycling System.

How the recycling system of Chicago can be improved?

Recycling amount distribution by location.

Correlation between recycling amount and demographical information.


After preparing the spreadsheets of the data and uploading them in iituim server database via PostgreSQL software as tables, shape files were imported to QGIS software where the contacts between the amounts and locations were made. Thus, equations for analysis were created.



22 wards have neither drop-off center nor blue cart system. 1,186,364 people living in this wards without any recycling service.

It is obvious that blue cart system is more efficient than drop-off centers due to its easiness. Travelling miles to throw the recyclables into the drop-off center instead of putting them into the blue cart in front of their house is a dissuasive effect for the people who do not live in blue cart covered neighborhoods.

It also seems like the people living in the north neighborhoods are more eager to recycle. Northwards can be proposed for the location of blue cart area future expansion.

For complete presentation with all the maps: RECYCLING 5.4.12


Urban Information Modeling Platform

This is a Final Project presentation for the Urban Information Modeling class at the Illinois Institute of Illinois. Both authors were interested in an interactive platform to view and create 3D building models. Originally, it was just meant to be another way for citizens to take part in the modeling of their cities in the United States and eventually across the globe.

As the project evolved, increased user participation and the integration of 2D geographic information became key elements. The capability to break down users into subgroups and allowing them to edit models on top of uploading originals was significant. Attached is the presentation with a complete outline of how the platform would be organized and displayed.

Urban and National Poverty Mapping in Vietnam

This is another rather incredible initiative undertaken in Vietnam which relates to Urban Information Modelling. Interestingly, this study looks at poverty levels not only on the urban scale but also on the regional and national scale. The article written about the modelling process is extremely interesting since much of the problems the developers of the program faced are similar to our own problems with formatting data.

For example, the article describes that the team originally had to account for regional differences in the data by adopting seventeen household characteristics which appeared in the earlier census data and then extrapolating them across the country. Trying to compare urban and rural populations, however, became problematic. According to the article:

“The team considered that this use of only two consumption models, one rural and one urban, for the whole country was somewhat unsatisfactory; it seems unlikely that returns to each of the rural (or urban) household characteristics would be the same across the entire country. As an alternative, the regression models included dummy variables for each of the regions to account for regional differences.” Through the inclusion of the dummy variable, however, much of the data ended up showing unrealistically large differences across the borders of two regions.

Another extrapolation made by the developers was necessary when raw data from the most recent census polls was unavailable to the public. As a result, the researchers could only purchase three-percent of similar data generated by the GSO (General Statistics Office)  and extrapolate the data across the country based upon geography and income levels as a proportion of that small percentage of data. As more and more organizations grew interested in refining their data, the developers were only able allowed to purchase thirty-three percent of the data in order to show the regional differences on a much finer level.

Apart from the technique, the mapping revealed newfound estimates of poverty headcounts for provinces, districts, and communes. “The maps provide a striking visual account of the depth of poverty in the mountains to the north, the upland areas along the coast, and the Central Highlands. They show much lower levels of poverty in the Southeast (where Ho Chi Minh City is located), other lowland areas, the Mekong River Delta, and the Red River Delta.” Though to anyone familiar with the geography of Vietnam or the war in Vietnam this is not incredibly uprising, the maps suggest that there is a strong correlation between poverty and geography rather than geography and administrative boundaries (a reversal of the antiquated, wartime viewpoint).

Lastly, the map representing poverty headcounts for provinces, districts, and communes revealed the heterogeneity within provinces in many parts of the country. “Many rich provinces have several poor districts, while the reverse is true for several poor provinces. By extension, many richer districts include a large number of poor communes.” Another map which shows the poverty density across Vietnam reveals that poverty is not high in areas of greater poverty density.

Anyway, the map has an interesting narrative regarding the mapping process: https://docs.google.com/viewer?a=v&q=cache:VLjmhReZMxkJ:siteresources.worldbank.org/INTPGI/Resources/342674-1092157888460/493860-1192739384563/10412-14_p261-286.pdf+mapping+poverty+vietnam&hl=en&gl=us&pid=bl&srcid=ADGEESiSMdXvMSMXGTHgANDuetBnMBPPLy6eHUDZ303RyB4XrOhjT91490fUdy4_c7WhnLejkcOwE0udH63bBQ4zcxYPC7itr22D3FS_2DGenAW8DL2AMny_uyZDdLNCfF-QyHCQ5mAs&sig=AHIEtbQENFbE06lzvNcpBxnXKMbdZFfJ1Q