How many more men than women suffered vehicular fatalities in the U.S in 2012?

According to the Center for Disease Control and Prevention’s Fatality Analysis Reporting System, more males died in vehicular accidents than females in every single state in 2012 (the latest year data is available). The graph below shows the rate of deaths of occupants involved in motor vehicle crashes by gender per 100,000 population in alphabetical order by state.

North Dakota ranked highest in male deaths at 29.3 and Missouri had the most female fatalities in the country, 14.2. In Illinois, the male death rate of 6.3 was nearly double that of females, 3.2.

Top 5 states for male vehicular death rates
State                       Death Rate (per 100K)
North Dakota                    29.3
Mississippi                        22.3
Wyoming                           21.9
Montana                             21.9
Oklahoma                          19.2

Top 5 states for female vehicular death rates
State                       Death Rate (per 100K)
Wyoming                           12.9
Montana                            10.9
North Dakota                    10.5
Arkansas                           10.4
Kentucky                           10.1

 

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DeathRate

Ask us how to visualize your research
For help visualizing your own research findings or seeing if your research lends itself to similar techniques including data acquisition and pre-processing of both quantitative and qualitative data, contact Nandhini Gulasingam at mgulasin@depaul.edu.

Vehicle Theft in Chicago

Even though vehicle thefts accounted for only 3.9% (10,099) of all crimes in Chicago last year, 62% of the stolen vehicles were recovered with severe damage says the Chicago Police department. Most often the vehicles are stolen by organized rings to be sold on black-markets or shipped overseas, and stripped for parts and resold to various body-shops, or are even resold to unsuspecting customers. In Chicago, 78.9% of the vehicles are stolen from streets, alleys and alongside sidewalks, 8.6% from buildings other than residences, 6.7% from parking lots, 5.5% from residences, and 0.3% from the airports.

The map below shows a hot-spot analysis of the communities that are most and least affected by vehicle theft. The visualization shows statistically significant (statistically significant is the likelihood that a theft is caused by something other than mere random chance) hot-spots in red where a high number of thefts occur and statistically significant cold-spots in blue where few or no thefts occur.

Communities most-prone to vehicle theft (not safe): Uptown (3) in the north, or Austin (25), Avondale (21), Logan Square (22), Hermosa (20), Humboldt Park (23), West Town (24), East/West Garfield Parks (26, 27), Near West Side (28), North Lawndale (29) in the west , or any south central parts of Chicago, namely Chicago Lawn (66), East/West Englewoods (67, 68), Greater Grand Crossing (69), South Shore (43), Auburn Gresham (71) are prone to vehicle thefts.

Communities least-prone to vehicle theft (safe): Edison Park (9), Norwood Park (10), Jefferson Park (11), Forest Glen (12), North Park (13), Dunning (17), Portage Park (15), Lincoln Square (4), North Center (5), Lincoln Park (7) in the north and Bridgeport (60), New City (61), Garfield Ridge (56), Clearing (64), Ashburn (70), West Pullman (53), Morgan Park (75), Beverly (72), Washington Heights (73), East Side (52) and Calumet Heights (48) in the south are least prone to vehicle thefts.
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VehicleTheft_StatSig_2015

 

Techniques Used
The above visualization includes 2 major types of spatial analysis techniques. The vehicle theft locations were geocoded using the addresses and then, Getis-Ord Gi* statistic was used to generate a hot-spot analysis to identify statistically significant clusters.

Implementing visualization techniques in faculty research
The image of the map reflects the different visualization techniques that might be used to effectively convey data or research conclusions to different types of audiences in various disciplines or industries. Visualizations can help identify existing or emerging trends, spot irregularities or obscure patterns, and even address or solve issues.

Ask us how to visualize your research
For help visualizing your own research findings or seeing if your research lends itself to similar techniques including data acquisition and pre-processing of both quantitative and qualitative data, contact Nandhini Gulasingam at mgulasin@depaul.edu.

CO2 Emission

Carbon dioxide (CO2) emissions are both natural and man-made. Natural sources include oceans, soil, plants, animals and volcanoes while human-related CO2 is emitted through deforestation, burning of fossil fuels such as coal, natural gases and oil for transportation, and energy for commercial, industrial and residential use. Although human-related emissions account for only 5% of the total, they have increased enormously overtime. According to the U.S. EPA, since 1970, global CO2 emissions have increased 90%, the major contributors (78%) being fossil fuel combustion and industrial processes, followed by deforestation, land-use change and agriculture.

While there are many ways to reduce carbon emission, the most effective is to reduce the consumption of fossil fuel. I pride myself for being environmentally conscious – reducing wastes by using energy-efficient products (furnace, light bulbs, etc.), taking public transportation, recycling and reusing things. Yet, using the “carbon footprint,” a calculator provided by the U.S. EPA, my annual footprint for home energy, transportation and household waste totaled 18,131 lbs., compared to the U.S. average of 24,550 lbs. for a single householder. However, this doesn’t include the CO2 emissions related to producing and delivering my daily consumption of certain goods (food, beverages, clothing, etc.) and services (restaurants, local grocer, etc.) including the amount of energy I use both at work (technology equipment, etc.) and commuting there (based on my 12-15 hours spent outside my home each day). This tool also revealed that just switching my washing machine from warm to cold water would cut carbon emission 150 lbs. per year and save me about $12. If you’d like to see your carbon footprint and/or identify ways to reduce consumption and save money, click on the EPA’s calculator here.

The following infographic shows the extent and distribution of CO2 emissions in the world, the U.S. and Illinois, including the carbon footprints of certain products.

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CarbonEmission_Infograph

Techniques Used
The above visualization includes 3 types of techniques:

Quantitative Analysis: A bar and pie chart were used to visualize quantitative data to show carbon emissions by various sectors over time and in 2013.

Statistical Analysis (GIS): Spatial analysis included two major techniques. The choropleth maps and classification methods were used to show the distribution of the emission levels globally and for the U.S.

Graphics: Images were obtained from Google and modified using Photoshop graphic design software

Implementing visualization techniques in faculty research
The image of the map reflects the different visualization techniques that might be used to effectively convey data or research conclusions to different types of audiences in various disciplines or industries. Visualizations can help identify existing or emerging trends, spot irregularities or obscure patterns, and even address or solve issues.

Ask us how to visualize your research
For help visualizing your own research findings or seeing if your research lends itself to similar techniques including data acquisition and pre-processing of both quantitative and qualitative data, contact Nandhini Gulasingam at mgulasin@depaul.edu.

Crime in Chicago

Among the 11,363 crimes reported in Chicago during the first 9 months of 2015, theft, battery, criminal damage, narcotics and assault ranked highest, totaling 68% of all reported crime. The infographic below shows a snapshot of crime in Chicago during this period.

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ChicagoCrime_Final

Techniques Used
The above visualization includes 4 main types of visualization techniques:

Text Analysis: The first image is a visual a representation of text data, specifically the word count of the type of crimes (i.e. frequency) displayed as a word cloud.

Spatial Analysis (GIS): The map uses an Inverse Distance Weighted (IDW) interpolation method to identify crime hotspots (in red). It also allows one to predict the frequency of crime at an unknown location based on the known values.

Quantitative Analysis: Two chart types were used to visualize quantitative data – a bar chart showing the crime counts of the major crime types for the most affected ward/community, and a bubble chart showing the number of crime by ward and community.

Implementing visualization techniques in faculty research
The image above shows different visualization techniques that might be used to effectively convey data or research conclusions to different types of audiences in various disciplines or industries.  Visualizations can help identify existing or emerging trends, spot irregularities or obscure patterns, and even address or solve issues.

A good example of one such implementation is John Conroy’s Legal Clinic project in the College of Law. This research project used a multi-pronged approach in which first, various visualizations were created to compare exonerations and false convictions in major U.S. cities. Later, the SSRC trained Conroy’s research assistants how to create an exoneration database and clean and convert data into mappable formats using various techniques.

Ask us how to visualize your research
If you want help visualizing your own research findings or wonder if your research lends itself to similar techniques including data acquisition and preprocessing of both quantitative and qualitative data, contact Nandhini Gulasingam at mgulasin@depaul.edu.