A STUDY IN CHICAGO RECYCLING SYSTEM

by Soledad Hernandez & Pinar Dursun

Problem

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.

Process

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.

Maps

Conclusion

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

Case Study: Noise Maps

A noise map is a graphic representation of the sound level distribution. Noise maps are used for calculating the areas affected by noise, determining the number of sensitive buildings affected by high noise levels, getting noise prediction models. The spatial database and spatial analyzing tools of GIS is useful to monitor the effect of noise and its impact.

European Union Member States are required to produce strategic noise maps in their main cities.

What are the necessities for a noise map?

3D city model of the area

Software packages: ARCVIEW/GIS, (ArcMap, Spatial analyst, 3D analyst, and ArcScene extension), standard noise calculation software, Point Cloud Mapper (PCM), FIELDS

The steps for 3D noise maps:

Step 1: Collection of data

Step 2: Building 3D city model, extracting and organizing the data about the objects of the 3D city model for noise calculation.

Step 3: Generating the 3D noise observation points and building 3D noise model.

These observation points represent location of virtual microphones where the noise levels are to be calculated. The acoustic indicators can be determined by computation or measurement methods. However, computation methods are widely preferred. Noise levels are calculated at each observation point by using noise calculation software.

When results are obtained, spatial interpolation was applied to give a continuous graphical representation of sound levels by using GIS tools.

3D noise map shows the volumetric view of noise levels on the road surface of study area.

These are the 3D noise map and noise contours of the Delft. Inhabitants on lower floors are more affected than on upper floors.

The size and position of noise barriers can be decided most optionally using 3D noise models. Higher barriers located close to the road are more effective to prevent the noise.

For more information about noise maps:

http://www.navcon.com/citynoisemap.htm

For Delft noise map:

http://www.gem-msc.org/Academic%20Output/Kurakula%20Vinay.pdf

For Paris noise map:

http://www.v1.paris.fr/commun/v2asp/fr/environnement/bruit/carto_jour_nuit/cartobruit.h

Fire Safety in High Rise Building

Chicago is full of the sky scraper and fire safety is one of the biggest issue in high rise building.  Fire poses a particularly serious threat in high-rise buildings.  First, it is difficult for fire fighters to reach the upper floors; for example, the highest fire truck ladder in Chicago only extends to the eight floors.  To extinguish blazes above that point, fire fighters must sometimes climb dozens of flights of stairs, dragging fire hoses and other heavy equipment with them.

Second, large buildings populations are difficult to rapidly and safely evacuate.  Since elevators do not offer a safe means of exit during a fire, thousands of people may be forced to descend crowded stairs. But the dangers are intensified in the noise, smoke darkness, and confusion of a high-rise fire, particularly for those attempting to escape from an upper floor.

 My idea behind this study is to create a building model based on cityGml and GIS  which can be helpful to emergency crew in emergency. Technologies such as GIS and CityGML can be used for analyzing and visualization of spatial data and 3d city model which is helpful in planning, simulation and training.

Data is available in different from following sources and it poses challenges for extracting useful information

  • Different sources of Data: city database. owner, architectural firms, emergency communication services
  • Different types of Data: Tabular data, spatial data, 3D models, shape files, floor plans etc.or Google Floor Plan Project
  • Different Format of Data: point, Date, co-ordinates etc
  • Various non-consistent schemas of Data, Eg, Some data is available in GityGML and other in GIS.

In order to maintain high integrity among various data sources we have to model our data based upon standards.  After that we can import our data into a common data store.  In case of building data, it can be model according to IFC standard or CityGML  standard for 3D building model and 2D data can be model in GIS format.

After we model data we use Data Mining techniques to extract useful information from our data. We can use different AI techniques to find different uses of our data.

UML diagram-Building Model

Scenario -1

  • If a Fire is detected on multiple floors of a high rise building then AI can help fire fighters to create smart evacuation plans on the fly using digitized floor plans and building sensors.

Scenario- 2

Planning of rescue operation

  • Eg, which window on 5th floor is accessible by fire ladder or where are building with large roof for helicopter landing.

Well, The newly unveiled concept project of Google, Google Glass, would be an ideal technologies useful for fire fighters.

Google Glasses Preview

a. Benefits of using Google Glass would be that fire fighters in case of emergency don’t want to look at another screen which is their phone, tablet or anything else.  Having a head mounted display would help them to focus on their jobs rather than on technology.  Moreover, this technology would also free their hands from using a device and they could help people.

b. Google Glasses technology  uses natural language processing which enables people to talk to a technology in human language rather than machine language.  Hence this kind of technology would be much friendlier to use, easier adoption rate, etc.

Tablet and Augmented reality Apps

a. Tablets with newer form factors and newer technologies could come very handy for fire fighters working in the field.  Having all the data they need on their tablet could help them make decisions quick which help save lives.

Vision of a building model using augmented reality

In conclusion, with use of urban information modeling and augmented reality we can not only reduce response time from fire station to disaster site, but also provide effective and safe escape from building and rescue operation.

With the use of CityGML and augmented reality, simulation of event can be useful to train new personnel such as fire fighters, police men, etc

Fire Simulation for Training

Urban Air Quality Mapping in Hanoi, Vietnam

Hanoi

I thought I would do a few Vietnam-related UIM pieces since I’m writing a paper this weekend on the War in Vietnam. This was an interesting article I found about air quality mapping in Ho Chi Minh City (formerly Hanoi) in North Vietnam. According to the article, the air quality is becoming poor due to a re-surging population. The population is expected to increase by 1.5-2 million by 2020 with an 11.2% rise in the economy. Interestingly, the use of cars in Hanoi grew by a factor of 30 from 1995 to 2005, leading to intensely dense, particulate matter suspended within the air.

Clearly, accurate information on the status of air quality is an essential component of Hanoi’s future, as well as for formulating action plans. The monitoring of air quality in Hanoi started in the early 1990s. Interestingly, the investment in air quality monitoring networks in Hanoi has increased far beyond average national levels; seven of the twenty automated Air Quality Monitoring sub-stations (15 stationary and 5 mobile) in Vietnam are located in Hanoi.

The study integrates a variety of graphic devices to analyze air quality. It includes maps which show the highest concentrations of particulate matter in the city parlayed with maps of traffic congestion, air quality cross-referenced with prevailing wind patterns, as well as maps which compare air quality with those of surrounding Asian cities. Other maps are based off of the following studies:

  • “MONRE – Collected hourly concentration of pollutants in the air in 2003 and estimated of traffic emission with resolution of 1 km x 1 km
  • JICA – Monitored 24 hour concentration of pollutant in the air at traffic intersections during August, 2005
  • SVCAP – Operated passive sampler network for January and February, 2007
  • DONREH – Monitored hourly pollutant concentrations at urban centers, industrial areas, and streets  during several months of 2006 and 2007
  • CENMA – Conducted monitoring from March to June 2007 at 6 industrial areas and 13 urban areas.”

The article also comments upon some of the challenges faced by the project in terms of project coordinators regarding public outreach and graphic display:

“In Hanoi , the participation of public on AQM related activities is also limited at this point. The online information from monitoring stations was designed to be disseminated through mass media such as newspaper, radio, television, and internet. However, at present, the operation of electronic information boards on displaying real time pollutants levels at DONREH and Department of Transportation is very sporadic and unreliable. Among all the agencies conducting air quality monitoring, the network of stations run by MONRE is most synchronized since 2002 (updating hourly data from the stations to the database center in Hanoi).”

To read Ngo Tho Hung’s PHD thesis on AQM in Hanoi, go to: http://www2.dmu.dk/pub/PhD_Hung.pdf

For a more concise article, go to: http://www.urbanemissions.info/model-tools/sim-air/hanoi-vietnam.html

Online disease surveillance system to be launched soon in India

This is exciting !!  I was just surfing online for some information related to healthcare , when i came across this article in one of the Indian Newspapers online , saying about the online tracking system ( through GIS ) for communicable diseases.

They are very soon about to initialize prototype for trial in 50PHC’s in southern part of India. The health officials would get an sms alert on their phones for location information of the diseases like swine flu and other communicable diseases.

This should surely help the government to take action sooner now !! Swine flu was a terror in India in the last two years.

article3288192.ece

Q GIS – Coordinates

When I tried to add a coordinate file in QGIS I realized that this program do not read degrees, minutes, seconds Coordinates (41° 00′ 18.48″ N, 87° 40′ 52.57″ W) therefore, it have to be converted into GPS Coordinates (41.003519, 87.791722)

Here is some Links that can help you:

Find Latitude and Longitude                                                                                            Convert Lat/Lon (option1)                                                                                              Convert Lat/Lon (option2)

Additionally, The link below is a tutorial about: How to Create a Shapefile from XY coordinate data using QGIS.                                                                                                 XY coordinate data to shapefile

By Soledad Hernandez

GIS for Fire Safety

I started looking at different aspects of GIS (Geographic information system) and usability of it  in disaster management especially man mad disaster such as fire in high rise building. GIS can help us visualize, understand, question and interpret data in many ways that reveal relationships, patterns, and trends in the form of maps, globes, reports, and charts. GIS is helpful to many people who are associated with urban planning, police department, fire department, government agencies, and many more. Using GIS, officials can pinpoint hazards and begin to evaluate the consequences of potential emergencies or disasters.

Chicago is full of the skyscrapers and fire safety is one of the biggest issues for the high rise buildings.  Under the City of Chicago Municipal Code high-rise buildings constructed after 1975 enjoy a high level of protection, since they must either be compartmented or sprinklered. But unfortunately, the Municipal Code does not require high-rise buildings constructed before 1975 to be sprinklered or compartmented, and therefore, a fire in one of these buildings is far more likely to result in casualties and property damage than one in a sprinklered building. The Commission has discovered that the rate of fire deaths in Chicago’s high-rise buildings is approximately 3.5 times greater than the national average.

Time is critical factor when emergency like fire occurs. To rescue occupant in timely manner is most important task for fire services. The time segment between fire ignition and the start of fire suppression has a direct relationship to fire loss.  The delivery of emergency medical serviced is also time critical. In most cases sooner trained fire personnel or emergency medical rescue personnel arrive, the greater the chance for survival and conservation of property.

GIS can quickly analyze and display a route from path a station or GPS location to the emergency call.  This route may be shortest path or the quickest path depending on the time of the day and traffic pattern. This information can be displayed to the dispatcher and on s mobile computer screen in the response vehicle.  Response time from fire station to the location and rescue operation within the building is critical to save life of occupant and reduce damage to the buildings.

In conclusion,  technology such as GIS can be really helpful in emergencies.

Esri Developer Summit 2012

This past week in Palm Springs, CA this annual conference was held. It is a gathering of developers to learn and teach about new tools used to create mapping applications or adding mapping to existing applications. The meetings cover various aspects of Esri’s ArcGIS system and gives insight on several other Esri products/technology.

The video linked here was from the session called “10 Killer Apps”. I only have this one video which is a demo of a UAV(unmanned aerial vehicle) Shark being controlled by a flex mapping system in an app. Although the mapping system isn’t “driving” something very relevant in this demo, you can see how this could be used for intelligence purposes in the future.

I will keep my eye out for more posts or reports on the conference regarding the 10 killer apps.

http://www.esri.com/events/devsummit/index.html

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.

http://www.cad-gis.com/n/publications/automatic_extraction_of_tents.pdf

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