A Framework For Dynamic Hand Gesture Recognition Using Key Frames ExtractionNEERAJ BAGHEL
Abstract—Hand Gesture Recognition is one of the natural
ways of human computer interaction (HCI) which has wide
range of technological as well as social applications. A dynamic
hand gesture can be characterized by its shape, position and
movement. This paper presents a user independent framework
for dynamic hand gesture recognition in which a novel algorithm
for extraction of key frames is proposed. This algorithm is based
on the change in hand shape and position, to find out the most
important and distinguishing frames from the video of the hand
gesture, using certain parameters and dynamic threshold. For
classification, Multiclass Support Vector Machine (MSVM) is
used. Experiments using the videos of hand gestures of Indian
Sign Language show the effectiveness of the proposed system for
various dynamic hand gestures. The use of key frame extraction
algorithm speeds up the system by selecting essential frames and
therefore eliminating extra computation on redundant frames.
Brain fingerprinting by ankit 2017............ankitg29
information about the brain fingerprinting technology . by EEG method by neuron firing and impulse of brain wave.P300 mean is 300-1000 m-sec brain wave, is better tech. than the polygraph test and other than than PET test. is basically depand the brain wave. is not the depand on the emotions and the pulse rate
A Framework For Dynamic Hand Gesture Recognition Using Key Frames ExtractionNEERAJ BAGHEL
Abstract—Hand Gesture Recognition is one of the natural
ways of human computer interaction (HCI) which has wide
range of technological as well as social applications. A dynamic
hand gesture can be characterized by its shape, position and
movement. This paper presents a user independent framework
for dynamic hand gesture recognition in which a novel algorithm
for extraction of key frames is proposed. This algorithm is based
on the change in hand shape and position, to find out the most
important and distinguishing frames from the video of the hand
gesture, using certain parameters and dynamic threshold. For
classification, Multiclass Support Vector Machine (MSVM) is
used. Experiments using the videos of hand gestures of Indian
Sign Language show the effectiveness of the proposed system for
various dynamic hand gestures. The use of key frame extraction
algorithm speeds up the system by selecting essential frames and
therefore eliminating extra computation on redundant frames.
Brain fingerprinting by ankit 2017............ankitg29
information about the brain fingerprinting technology . by EEG method by neuron firing and impulse of brain wave.P300 mean is 300-1000 m-sec brain wave, is better tech. than the polygraph test and other than than PET test. is basically depand the brain wave. is not the depand on the emotions and the pulse rate
Eye Movement based Human Computer Interaction TechniqueJobin George
Eye movement-based interaction is one of several areas of current research in human computer interaction in which a new interface style seems to be emerging. In the non-command style, the computer passively monitors the user and responds as appropriate, rather than waiting for the user to issue specific commands. In describing eye movement-based human-computer interaction we can see two distinctions, one is in the nature of the user’s eye movements and the other, in the nature of the responses. In the world created by an eye movement based interface, users could move their eyes to scan the scene, just as they would a real world scene, unaffected by the presence of eye tracking equipment movement, on the eye movement axis. The alternative is to instruct users of the eye movement based interface to move their eyes in particular ways. On the response axis, objects could respond to a user’s eye movements in a natural way that is, the object responds to the user’s looking in the same way real objects do. The alternative is unnatural response, where objects respond in ways not experienced in the real world.
Now a days Eye tracking technology is applied in many fields like automotive defense and medical industries. The fields of advertising, entertainment, packaging and web design have all benefited significantly from studying the visual behavior of the consumer. Every day, as eye tracking is used in creative new ways, the list of applications grows.
Privacy and Data Security in Data miningAbhishek L.R
Presentation on Privacy and Security in Data Mining. Where it contains the information about the Mining Process, how it is done, what are the major threats in the process.etc...
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
A Simple Presentation about Finger vein Authentication System.It Details about Finger vein authentication system with easy words & pictures.It is an effective ppt prepared with essential informations about finger vein authentication system.It should be useful for students as well as programmers.
Applications of Biometrics in Technologyiamsanjayk
Biometric in the field of Computer science ! This is a powerpoint presentation prepared as a first year participation in college presentation competition. Topic - Applications of biometrics in technology. This was my first attempt. Hope it comes in use for people in need of a simple presentation.
Eye Movement based Human Computer Interaction TechniqueJobin George
Eye movement-based interaction is one of several areas of current research in human computer interaction in which a new interface style seems to be emerging. In the non-command style, the computer passively monitors the user and responds as appropriate, rather than waiting for the user to issue specific commands. In describing eye movement-based human-computer interaction we can see two distinctions, one is in the nature of the user’s eye movements and the other, in the nature of the responses. In the world created by an eye movement based interface, users could move their eyes to scan the scene, just as they would a real world scene, unaffected by the presence of eye tracking equipment movement, on the eye movement axis. The alternative is to instruct users of the eye movement based interface to move their eyes in particular ways. On the response axis, objects could respond to a user’s eye movements in a natural way that is, the object responds to the user’s looking in the same way real objects do. The alternative is unnatural response, where objects respond in ways not experienced in the real world.
Now a days Eye tracking technology is applied in many fields like automotive defense and medical industries. The fields of advertising, entertainment, packaging and web design have all benefited significantly from studying the visual behavior of the consumer. Every day, as eye tracking is used in creative new ways, the list of applications grows.
Privacy and Data Security in Data miningAbhishek L.R
Presentation on Privacy and Security in Data Mining. Where it contains the information about the Mining Process, how it is done, what are the major threats in the process.etc...
This slides about brief Introduction to Image Restoration Techniques. How to estimate the degradation function, noise models and its probability density functions.
A Simple Presentation about Finger vein Authentication System.It Details about Finger vein authentication system with easy words & pictures.It is an effective ppt prepared with essential informations about finger vein authentication system.It should be useful for students as well as programmers.
Applications of Biometrics in Technologyiamsanjayk
Biometric in the field of Computer science ! This is a powerpoint presentation prepared as a first year participation in college presentation competition. Topic - Applications of biometrics in technology. This was my first attempt. Hope it comes in use for people in need of a simple presentation.
Student #1 I have chosen to write about the history of data anal.docxjohniemcm5zt
Student #1
I have chosen to write about the history of data analysis for the Los Angeles Police Department. While I currently reside in Colorado Springs, Colorado and work as a deputy sheriff in Denver, Colorado I grew up in the greater Los Angeles area and I know that they should have a large amount of data to draw from.
Currently the Los Angeles Police Department uses COMPSTAT to compile their data. They have a unit, known as the COMPSTAT unit, whose sole job is to compile crime statistics and analyze the data (Los Angeles Police Department, 2016) COMPSTAT is short for computer statistics. COMPSTAT was developed by Police Commissioner William Bratton in 1994 for use by the New York Police Department. According to the University of Maryland by the year 2000 over a third of police agencies with over 100 officers were utilizing some sort of COMPSTAT like program (University of Maryland, 2015). In 2002 William Bratton became the Chief of Police for the Los Angeles Police Department and brought with him the concept of COMPSTAT. During the first six years of his tenure Los Angeles saw a steady decrease in the cities crime rates thanks largely in part to COMPSTAT policing.
Mean, mode and median play a large part in analyzing criminal data. The mean is the average number. An example of this for crime data analysis would be in neighborhood C there was 14 robberies committed on Monday between 1 and 3 AM, 17 robberies on Tuesday at the same time period and 9 on Wednesday during the same time period. The mean would be 13.3 robberies per night for those 3 nights. Knowing this is high for the city the data could be used to justify extra police presence in Neighborhood C. An example of the mode would be if in the same neighborhood in the same week there were 17 robberies on both Friday and Saturday, 12 on Thursday and 11 on Sunday. The mode would be 17 and it would also be a reason to add extra police presence in the neighborhood until a significant decrease was seen in the amount of robberies taking place. Finally we come to the median. This is simply line the numbers up for the week and take the number that falls in the middle. In the case of the robberies occurring in neighborhood C the number would be 14. All of this data can be combined to show watch commanders and captain’s areas where they should be focusing their officer’s time. If there is a neighborhood that has seen only one or two robberies during the week, it is definitely not in as much need of a heavy police presence as Neighborhood C is.
Student #2
Beginning in the mid-1990’s, police in New York began to run statistical analysis of the city’s crime reports, arrests and other police activity known as COMPSTAT. Law enforcement agencies since this analysis began, has implemented their own data-driven approaches to tracking and adapting to crime trends. The LAPD is both heavily armed and thoroughly computerized. The Real-Time Analysis and Critical Response Division is its central processor..
This is the analytics report of big data set of Chicago Crime. I had generated this report as a course assignment while studying Master of Technology at FedUni Australia.
An Intelligence Analysis of Crime Data for Law Enforcement Using Data MiningWaqas Tariq
The concern about national security has increased significantly since the 26/11 attacks at Mumbai, India. However, information and technology overload hinders the effective analysis of criminal and terrorist activities. Data mining applied in the context of law enforcement and intelligence analysis holds the promise of alleviating such problem. In this paper we use a clustering/classify based model to anticipate crime trends. The data mining techniques are used to analyze the city crime data from Tamil Nadu Police Department. The results of this data mining could potentially be used to lessen and even prevent crime for the forth coming years
GIS is a discipline that heavily relies on data. In this presentation we highlight all the geospatial data sources for crime mapping.
Visit https://expertwritinghelp.com/gis-assignment-help/ for quality gis assignment aid
Utilized the Twitter WPF client to extract data based on Hashtags and fed to sentiment140 for sentiment analysis.
Loaded sentiment analyzed tweets in Azure using event hubs and performed analysis using SQL in stream analytics
Stored the analyzed data in Azure Blob Storage and visualized the outcomes of analysis in real time using Power BI
Identified the pivotal reason for the cause of death amongst the residents in US based on the age group
Forecasted the deaths in near future considering the predicted deaths and observed deaths records of previous years.
Integrated data from twitter for major death causes using Hashtags and the twitter API calls and analyzed sentiments
Explored the dataset on NFL 2014 to discover the performance of all the teams throughout the seasons.
Identified and visualized the factors which hold the major ground for a team to be successful in the tournament.
3D- Dimensional visualization for comparison of the first down amongst the top 5 teams was built using plotly.
Analysis on an decade of data relating to start-up which would guide the budding start-ups towards the way of success and also provide them the right place for maximum funding.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
2. Dataset:
The dataset taken for analysis is “Crimes 2001 to Present” which deals with the crimes
committed in the city of Chicago from 2001 to till date. The rich data provides us details about all
the crimes except for the murders. Comprising of 60 lakh rows and more than 15 columns this
dataset gives the opportunity to identify various insights for the city of Chicago pertaining to crime.
Data regarding the various types of crimes that are committed (Primary Type), location where the
crime is committed both geographical and also the public location, their corresponding district and
ward number is furnished. Each crime record also consists the specific date, time and the year in
which the crimes are committed which would helpful in analyzing the trend of the crime over the
years. In addition to the location and the district data, this dataset also provides us the BEAT code
(A beat cop is a law enforcement officer who walks, rides, cycles, or drives in a specific
neighborhood that becomes known as his or her “beat.” [1]) which would help in fetching more
accurate perilous locations across the city. The IUCR (Illinois Uniform Crime Reporting) and FBI
code is also provided in addition to the above mentioned data. Summing up all these data very
productive information could be extracted which could help in improvising the safety at Chicago.
Dataset URL: https://data.cityofchicago.org/view/5cd6-ry5g
Data Cleaning:
Missing values:
Before:
3. After:
As the values in the other fields are required for the analysis the records are not deleted,
instead they are fed an “Unknown” with which the analysis can be continued successfully.
Irrelevant data:
Removing the column “Description” as the data is irrelevant to the analysis performed.
Duplicate Records:
Before:
4. After:
Contradicting Values:
Before:
There is no district named 31. The maximum district in Chicago is 25, thus replacing with
the appropriate district based on the Beat Code.
After:
Misfielded Values:
The issue of Misfielded values is not faced with this dataset.
5. Data Visualization:
Top 5 Research Question:
1. Location which are the most Unsafe in Chicago?
The Visualization above gives us a clear insight on the most perilous locations in the city
of Chicago. The total crimes committed from the year of 2001 to 2016 is 6 million of which more
than 1.6 million is reported to have taken place in the streets. This signifies that almost 25% of the
crimes committed is in the streets of Chicago. Another interesting fact to make a note of is the
second dangerous location which is the residence, which constitutes almost 1 million of the entire
crime. With this data, more precautionary measures could be taken to make the places safer.
6. 2. Total arrest made against the total cases registered?
This simple Pie chart gives us the information about the total arrests that are made. This is
an interesting fact to be noted as it states that against the total of 6 million cases registered over
the 16 years only 25% of the cases (i.e) only 1.7 million cases alone are recorded with the criminals
being arrested. Rest of the 75% have recorded the arrest as false which an interesting and
dangerous fact.
3. District recorded with the highest criminal activity?
7. The visualization is done to get more clear insight of which district is recorded with the
most criminal activity. As the dataset, had provided the data of the district number a “calculated
field” has been used to check the district number and to substitute it with a district number and
name. This analysis lets us know that “District 8 Chicago Lawn” has been recorded with the most
crime over the years. This analysis also let’s know the count of the different type of crime that are
committed over the years. This analysis give us a picture that “Theft” is the most committed crime
in almost all the district.
4. Which type of crime is committed the most by year and quarter? And has the crime
reduced or increased over the years?
8. From the above chart, we get to know that the most committed crime of all the time is
Theft. Analyzing the graph based on month and quarter an underlying fact is revealed which states
that the crime rate is generally high in the 3rd quarter. Analyzing more specifically the crime rates
are generally high during the month of august except for battery. All the crimes have always
reduced compared to what it had been in the beginning of the year which states that crime rate has
decreased over the years. A “parameter control” has been given which can be used to change the
years and analysis can be made for each year.
5. Growth or decline of the criminal activities by years?
Analyzing the trend of crime from 2001 to 2016 with the data provided above we can
conclude that the crime rates have fallen over the years. The highest number crimes were
committed in the year of 2002 of about 4.8 million which has drastically reduced over the years to
2.6 million by the year of 2016. There happens to be a 46% decrease in the crime rate from 2001
to 2016. With the help of the “Reference Line” we also get to know that the crime rate of the last
4 years is well below the average crime rate. Based on this fact a forecast has been done to
9. approximately predict the crime rate of the future. This prediction reveals that the crime would
decrease in the upcoming years to a greater extent.
Additional Analysis:
1. Most notorious BEAT in the district with the highest crime rate
Upon analyzing the district with the maximum crimes, further analysis was made on the
most notorious district. District 8 which had recorded the highest crime rate is further analyzed
based on the details of the BEAT. The grouping technique has been used in this analysis where the
BEATS are grouped based on their district except for the district 8. From this analysis, the BEAT
number 823 has been ranked the 1st for being for having committed more number of crimes.
2. Geographic visualization of the criminal activities of “District 8”
10. This visualization is done to bolster the results of the above-conducted analysis. This
analysis gives us the clear picture of which region in the “District 8 Chicago Lawn” has been the
most unsafe location. From the geographical visualization, we can infer that the southeastern part
of the district has the more criminal activities. This bolsters our previous analysis which state that
the wards 823, 825, 831 and 832 have been recorded with the most criminal activities and these
wards fall under the southeastern part of the district. Further with the use of the parameter control
for year and district we will be able to visualize the criminal activities in various districts over each
year.
Dashboard:
11. Storytelling
This analysis of the dataset “Crimes 2001 to Present” of the city of Chicago was done to
get an insight on the criminal activities taking place in the city of Chicago. An initial research was
done which revealed that only a very minimum of information regarding the criminal activity is
available. The article Safe and Dangerous Places in Chicago Lazar, Louie. [3] gives us only a brief
idea on the dangerous and safe localities in Chicago. Moreover, this information is very limited to
a maximum of 5 places. This analysis would help in getting greater insights regarding the criminal
12. activities in the city of Chicago. This analysis commenced with analyzing the criminal activities
over the years. The results of this analysis revealed that over the period of 16 years from 2001 to
2016 the crime rate was at the peak in the year of 2002 with a record high of 4.86 million. It is
visible from this analysis that the crime rate has reduced as a constant rate over the years. The
crime count in the last four years have been well below the average crime count of 16 years. A few
recommendations were also made by the cure violence [4]. The article “The Truth about Chicago
Crime Rates” by Bernstein, David and Isackson, Noah [5] has also mentioned that the crime has
decreased from the year of 2012. The article is also specific in stating that, upon the Police
superintendent Mr. McCarthy taking charge in 2012 the rate of crime has fallen by leaps and
bounds. From this analysis, it is evident that there is a 46% decrease in the crime rate from 2001
to 2016. A forecast of the trend line predicts that the crime count would further reduce making
Chicago more safer. Further research is done on the top 5 crimes which are theft, battery, criminal
damage, narcotics and the other crimes. This analysis brings to light the that the criminal activity
is generally peak in the third quarter. Analyzing in depth reveals the fact that the crime is generally
high during the month of august except for criminal damage. The analysis that is currently
displayed only represents for the year of 2001 which is further segmented into quarter and months,
this analysis allows us to gain a clear idea of the criminal activities in each year. With the help of
the parameter option provided the user can easily fetch the data for every year. This analysis also
brings out another insight that even though there is a decrease in the crime count, theft has always
remained to be the major crime which is committed in all the years. Analyzing so much crime
records made me inquisitive about the most perilous location in Chicago, this made me conduct
the next research which was to find which location has been recorded with the most crime
reporting. This analysis revealed that the streets of Chicago has been the most perilous location all
13. the time. The report shows that about 1.6 million of the crimes are reported to have happened in
the streets of Chicago. This accounts to be almost 25% of the entire crime committed which is 6
million. Following the streets, the next place which has the most crime count is residence followed
by apartments and sidewalk.
As the dataset provided us more information, this analysis was made a little deeper. With the help
of the district numbers furnished in the dataset the corresponding district names were found which
were appended to the district number using the calculated field. When analyzing the data, it was
evident that District 8 “Chicago Lawn” has the highest record of crime consistently. By analyzing
the chart, we find that Theft has been the most committed crime in general, but for district 11
“Harrison” the most reported crime is narcotics. This chart also tells us that the crime count has
14. decreased over the period. Upon analyzing the district with the most crime report with the help of
the BEAT number, analysis was done on district 8 “Chicago Lawn” to discover which BEAT has
caused the most trouble. This simple bar chart with the help of grouping helped us to fetch the
information which was needed. As per the chart the most notorious BEAT in the 8th district
“Chicago Lawn” is BEAT number 823. This BEAT has a crime report of 41,908 is which almost
20% of the entire district crime rate. A geographical analysis was made with the help of the latitude
and the longitude data given. This was done to bolster to the previous analysis and find which
region in “Chicago Lawn” has been reported with maximum crime. Upon plotting we can conclude
that the crime is more predominant in the southeastern part of the district. The final analysis was
done to calculate the total arrest against the total number of cases registered. This gives us an
interesting fact which states that the total count of the arrests made is 1.7 million against the total
number of cases 6 million. This is so interesting because of fact that only 25% of the cases
registered have the criminals arrested which is a very low figure. The article “The truth about
Chicago Crime Rates” [5] give us more information about the issue. In this article, it is discussed
that there are some crimes which are initially reported to be murders are later called to be homicide.
The authors blame the Chicago police department for this fact. However, the same article also
states that there is a decrease in the crime rate which bolsters our analysis. The above analysis
gives us insights on various aspects of the criminal activities in the city of Chicago, we have gained
more information on the most dangerous places in Chicago, the most notorious districts and also
the most notorious BEAT among them. We have also seen how the crime rate have decreased over
the years and the forecast of the crime for the next 5 years. This could be used by the Chicago
police to educate the residents and visitors of Chicago and provide them more safety by
concentrating more in the dangerous areas.
15. References:
1. McMahon, Mary. Mitchell, C and Wallace, O. “What is a Beat Cop” WiseGEEK,
http://www.aol.com/article/2010/08/31/safe-and-dangerous-places-in-chicago/19605029/
2. Cop, Chicago. “Maps” ChicagoCop,
http://chicagocop.com/html/documents_archive/maps.html
3. Lazar, Louie. “Safe and Dangerous Places in Chicago” Aol,
http://www.aol.com/article/2010/08/31/safe-and-dangerous-places-in-chicago/19605029/
4. Cure, Violence. “Program Profile: Cure Violence(Chicago, Illinois)” Crimesolutions,
https://www.crimesolutions.gov/ProgramDetails.aspx?ID=205
5. Bernstein, David and Isackson, Noah. “The truth about Chicago Crime Rates”
Chicagomag, http://www.chicagomag.com/Chicago-Magazine/May-2014/Chicago-
crime-rates/