Chicago Urban Migration-Wigdan Al-Guneid,Sakshi Aggarwal


Obtain the relationship between the quality of neighborhood and the migrations of people of people to them. Through the analysis of attributes such as health care, education, transportation accessibility, and crime rate, we were able to indicate the popularity of a neighborhood and wither some attributes are more important than others in ranking the value of a property. Our analysis will be focusing on the relationship between housing value in 6 main neighborhoods from different location of Chicago and amount of people who relocated from or to them.

The neighborhoods chosen are:

-China Town (District 31)

-Hyde Park (District 41)

-Lincoln Park (District 7)

-Logan Square (District 22)

-Garfield Ridge (District 56)

1- The data available with us should be translated into maps that illustrate the quality score for each of the chosen neighborhoods.

2- The analysis of the data should be driven from maps that are done using the CSV files, and shape files downloaded from Chicago website.

Using the Software QGIS, PG Admin will help in driving maps with analysis of Chicago neighborhoods.

In order to understand the pattern of migrations within the neighborhood’s of Chicago, the study had to include a method of scoring the quality of a neighborhood. A quality would be driven from series of calculations and equations that will be translated later into queries in PQ Admin II then into maps.


School Equation

School safety score+ family involvement score+ Parent engagement score+

College eligibility+ college enrollment

______________________________________             = School Score


Hospital Equation

Accessibility of hospitals+ number of hospitals

______________________________________= Hospital Score


Accessibility Equation

School safety score+ family involvement score+ Parent engagement score+

College eligibility+ college enrollment

___________________________________________________________= Accessibility Score


Neighborhood score Equation

Home value index +School Score +Hospital Score + Accessibility Score +

___________________________________________________________= Quality of




After   checking the accuracy of the data in the tables that are done with POST ADMIN II, queries were made to connect the tables we have to the QGIS .This step is important in order to match the data to the shape file of the neighborhoods of Chicago city.

After making the maps, analytic comparison driven from the visual information in the maps, and also from the value of the quality averages driven from the neighborhood’s score equations.


The quality scores showed that the higher quality neighborhoods were Lincoln Park, followed by Logan Square, Garfield Ridge, Hyde Park, China Town, and Avalon Park.

This conclusion however, matched the Real Estate value of Lincoln Park and Logan Square, and Avalon Park only. Hyde Park and China Town had different real-estate values that didn’t correlate necessarily to the neighborhood quality.

This led us to a conclusion that there are other reasons such as ethnic backgrounds of populations living there. China Town is a neighborhood that is heavily populated with Asian communities, Same as Hyde Park that is also heavily populated with African Americans.

The study was only focusing on amenities that a neighborhood would offer and if they would affect the decision of selecting a neighborhood or not.


Q GIS 1.7.3

Is any one able to find the QGIS 1.7.3 and install it?

All what I can find in there website is the 1.7.4 version, and I understand from the class that it got problems.


Case study on Automatic Extraction of Tents during Hajj from Airborne Images

Hajj Pilgrimage

This is a case study in which the usage of GIS is beneficial for temporary settlements such as the tent cities built for the Haj Pilgrimage in Saudi Arabia (Islamic Pilgrimage). This annual religious event brings around 5 to 6 million pilgrims every year to Mecca and with the huge number of pilgrims, the use of urban space is a major concern for engineers, urban designers, and urban planners. Pilgrims must stay in the Holy Area (Arafat) for one day as part of Hajj rituals. For this reason, pilgrims are housed in lightweight temporary structures: tents. In Arafat, these tents are constructed before each Hajj season. The arrangement of these tents differs from one year to another and from location to location. For the spatial and temporal constraints of ritual happening in Arafat, space optimization is an important issue. The extensive demand for a rapid, automatic, and high quality algorithm for feature extraction has been the subject of much recent research.

This study present an approach for using airoborne images, tents were detected and extracted, then were used calculate the areas covered by tents. It utilizes the intensity in digital images in two stages. First, it classifies tents from other features in Arafat’s environment. Second, it calculates the number of tents based on image matching subroutines. This can evaluate the design and planning of tents’ layout and space optimization. Using this automatic approach, the number of pilgrims in a tested area can also be estimated according to the average capacity of one-meter squares covered by tents. Moreover, services, utilities, and transportation needs can be determined more precisely. An actual sample area in Arafat during the Hajj season is used to test the approach developed in this research.

An arial image of Arafat,showing its spatial limits.(Courtesy;space Imaging Middle East)

What is location based marketing?

I found it fascinating of how marketing field draws alot of information from Census and demographics.Now with Social media,and accessibilty of information from cellphones, it became an opportunity for marketing professionals to taylor their ads. based on the location of their consumers,and based on their buying patterns. This could be buisness ads.

, discounts and coupons. Marketing professionals see it as the new generation of consumer advertsing.

Case study: Events, Social Media, and urban information modeling

My case study  mainly focused on three main events that happened during 2011, which were the Egyptian revolution, Japan’s Tsunami, and Occupy movement in New York. Those events could be like any events happened in the world, except that regular people started to publish and make news within the big headlines.Users of social media websites, mainly twitter, where the news publishers,reading the whole picture of the event and through them,where able to understand the social context of the events.

Besides, I was focusing on the news feeds, which are instant tweets coming from the users at that certain place of event. The collection of tweeps (people who tweet) in the certain geographical location starts a hotspot or a hub of information production. Without understanding where this information is coming from ,geographically, the information will not be complete and could be misleading.

This fluid of information coming from people through social media could be a valuable source in understanding human landscape, the social complex structures and relationships between people and how they interact with each other. It is far more vibrant and interactive than other information sources such as the census since it shows how people with similar opinions interact together and “geographically” presents the human terrain of ideas. Specialists in campaigns, marketing, advertising, and socio cultural analysts could benefit from such information.

Harvesting social media feeds is through three operations:

-Extracting Data from social media servers using API (Parsing, integrating) and storing this data in a resident data base.

-Analyze the data

Twitter content revealed emergence of socio-cultural hot sports ,and provides advanced warning of forth coming events, as was the case with Tahrir square reference in Arab spring events of spring 2011.It also offers a mechanism to obtain a rapid assessment of the impact area of natural disasters as demonstrates by data collected during Japan’s Tsunami .It provides unparalled situational awareness by supporting the monitoring of evolving events, as was the case with the Occupy Brooklyn bridge experiment.