Tracking low Profile Criminal Activities

  A lot has been talked about how we use technology and data to combat the crime rates and how we can reduce that, But we often forget about the non-traffic crimes which are of low criminal in nature, such as spitting on pathways, urinating in public places, Public Harassment etc. Some are recorded while most of them are unrecorded. They are unrecorded because nobody raises questions or file complaints against such crimes. And thus this gives these low key criminals the audacity to do repeatedly without any fear. This sort of crimes is generally done even in peer groups and circle of friends. Thus, even though people do have a problem in the same group they generally shy away from complaining. These low key Criminals are even young educated students (undergrad/grad) and professionals. 

How can we engage the community or educate them to take action against such crimes and how they can be involved using technology. How can we stop these criminals from doing it again and understanding the seriousness and affect it has on them as well as the community or neighborhood in general 

Step I :

As said before most of these low profile activities are not even recorded but there are some which go through lawsuits, and WPRDC has this data recorded and up on their site. Thus I use this data to explore the areas which face the most number of crimes and what is the type of crime. What age, sex and race these criminals belong to so, as finding ways to educate them and also make people aware of these people. 

The WPRDC dataset, consist of the criminal activities recorded by Pittsburgh Police from 2016-2017.

Datasets Used : 

1) Non-Traffic Citation, Pittsburgh - https://data.wprdc.org/dataset/non-traffic-citations

2) Police Blotter Data - https://data.wprdc.org/dataset/uniform-crime-reporting-data

3) Pittsburgh Neighbourhood Shapefile - https://data.wprdc.org/dataset/pittsburgh-neighborhoods1

As the number of crimes listed in the data set was a broad range, grouping the similar kinds of crime into in the dataset was a challenge. While sorting the dataset, I realized that these crimes locations were most nearby to each other, thus in order to check, I used the Clustering analysis to visualize the areas most affected.  

From the Cluster Map shown above, it can be extrapolated that the Cluster number 1, 2 and 3 are the ones with the maximum number of complaints from a time frame of 15 August 2016, to 6 December 2017. There are very few complaints before August 2016, approximately less than 30. This can ultimately be also inferred as either the community is taking actions against such crimes since August 2o16 or there is a rise in the number of crimes due to certain reasons. Further the age group involved in the crime is between 20-30 years and majorly committed by males which is more than half of the crime committed by females. These crimes are generally by whites, followed by black people, a very few Hispanic and Asians are involved. Lastly, it can be noted that these three major clusters 1,2 & 3 are mainly concentrated in Southside Flats followed by Central Business district, Central Oakland, and North Shore. Then there are certain areas in Shadyside and Penn Hills forming small clusters. All these concentrated clusters are generally around restaurants, bars, and pubs. These are the neighborhoods which people visit from all over the Pittsburgh. 

Thus, to further study these majorly affected zones. I mapped the data with the different crimes and its location. To see if there is a relation between the crimes happening in these clusters. 

It can be said from the map that the three main low profile crimes repeatedly noted are public drunkenness followed by hazardous public behavior and urinating/spitting/defecating in public spaces.

To further check the type of criminals involved in these places, I mapped it with age. 

Thus, I will consider these broken areas to come up with strong public engagement strategies with the help of this data

Thus, one can see that from the above map that, the overall age group of criminal mainly involved is between 20-35. Also in the four major areas are also affected by criminals of age group 20-40 approximately. 

From the above maps, it can be inferred that all these three activities are occurring in the same places, with same age and sex ratio. There are many conclusions which can be drawn from this. 

1) It could be that people are getting drunk generally urinate and defecate on streets and perform the hazardous public behavior. 

2) It could be altogether different people as well doing different activities. 

Further, I mapped individual crimes to see the intensity and how they relate to the different crimes in same areas.  Because South-side, North Oakland and North shore and business district have people coming from all over the Pittsburgh. Also to learn as to how can we Educate people from all these places. 

It can be inferred from the above map there is almost an even mix of all these crimes in the areas majorly affected with a little uneven in Northshore which is likely because of the widespread of public activities and places in Northshore. 

It can be noted that the major of these areas are interlinked it seems on the surface except North shore where hazardous public behavior seems to be not of the major issue but Public urination and drunkenness is a major issue. 

STEP II : 

It was hard to map the time of these incidents but It could be a strong analysis if further the time of these incidents would be mapped to check the hypothesis whether people are really getting drunk and then getting involved in these haphazard activities and public urination or these are completely independent events.

Thus keeping in mind all the analysis done above, for the purpose of this project, I shall form various strategies considering these as independent as well as dependent events. 

  Strategy 1:   

A community group in Hamburg, Germany tried a new solution to spread awareness with public peeing. St. Pauli is a popular nightlife district, and the neighborhood was tired of partiers peeing in their streets. Fines didn’t help. So the group bought Ultra Ever-Dry, a hydrophobic coating that creates high points on a surface which forcefully repel liquids — in this case, directing urine back onto a person.

With help of the spotted places in the above maps, the walls from these areas could be painted with these special paints and the painting could be an awareness of being publicly drunk and the outcomes. 

Additionally, walls could also be painted heat maps showing the affected areas and a blank map for people to mark these as well as various other low profile crimes happening in the locality.

This way, people will be aware of gradually we are contaminating the city and at the same time help gather data. 

COST: The paint is a fairly cheap, costing a few hundred dollars per wall. 

  Precedent :

https://melmagazine.com/can-pee-resistant-paint-stop-the-scourge-of-public-urination-82cfae3d2778

Strategy 2 :  

A local artist and a resident of a neighborhood in San Francisco were fade up of crossing poops, syringes and realized that it was easy for locals to become blind to all the grime, so he decided to bring attention to it through this "whimsical art project." as shown above. 

In our project, we can use such art piece, using poops, broken alcohol bottles (Hazardous public behavior) and display them on streets to bring the attention of the people or similar whimsical artwork could be used. 

Cost: Few hundred dollars. 

Precedent : 

http://hoodline.com/2016/08/local-artist-turns-soma-feces-syringes-into-mouthwatering-art?utm_source=story&utm_medium=web&utm_campaign=stories

Strategy 3 : 

Engaging the local bars and shops in the locality to take ownership of nearby public spot which is endangered and also it can take note of the people visiting bars. 

A creative initiative of bar owners and other commercials to make a great difference. 

Also, a platform for government body handling 311 data, can come with an app to bring together people concerned who would want to help the city combat such problems. 

Also, people can anomalously, register spots on a map on this app. 

This would involve a high cost but it would be suggested as it would tackle other issues as well. 

Precedent:

 http://promisetracker.org/

STEP III : 

Recommendations : 

Studying from above analysis, it could be suggested to deep dive into the analysis in terms of time and it could help strategize if certain things happen only in the night then, more engagement solutions need to be thought of which works better in the night.