Introduction
The subject of the study has permitted to obtain a linear model. A model is entirely good to test or stress a system. In this idea , I propose some test suggestions.
I choose to follow the city of Chester in Pennsylvania that is our maximum value for the violent crimes in USA and the mean of all the violent crimes in USA.
In our study,the actual violent crimes of chester city is 4877 for 100k habitant .It should be different today.
Our model estimate the value to 3916 violent crimes. An error of 20%
Poverty and crime rose as the chester city declined since 1950.
The racial makeup of the city was 17.2% White, 74.7% Black, 0.4% Native American, 0.6% Asian, 0.1% Native Hawaiian, 3.9% of some other race, and 3.0% from two or more races. 9.0% were Hispanic or Latino of any race.
The model estimate the mean of the violent crimes in USA to 1174 violent crimes to 100k habitant
Some question could be given to stress our model .
Immigration reducing
The first I propose : is reducing recent immigrant a solution ?
If we raise 10% of the recent immigration the model give this result :
on chester city the violent crime appear stable to 3917 violent crimes
on the usa the mean increase to 1186(+1%) violent crimes per 100k.
Recent immigration appears not a problem for the security.
In second : Is reducing all immigrant generation a solution ?
Chester city decrease the violent crime to 3915 violent crimes per 100k .
On the mean in USA the violent crime decrease to 1171 violent crimes.
The result is not signifiant.
The idea of reducing immigration is not a good idea and not offers a good result.
Education
Is giving a better education a solution ?
If we decrease by 10% the not graduate or less graduate the model is offer this result:
Chester city increase their violent crimes value to 3922.
And the mean violent crime in USA to 1181.
The result is not corresponding on the simple idea that a better education can really change the given actual criminality.
Ecthic
Is increasing ethnic mix a solution ?
If we increase the white population by 10% , the model is offering this data:
for chester city : 3916 violent crimes . No impact on the criminality.
for mean in USA : 1166 violent crimes
As other result,we can improve the criminality in adding more white population.
Housing
Is decreasing house boarded a solution?
House boarded is a ravage in a city. Offering inglorious area and give bad image on the land this house need to be cleaned.
If we decrease by 10% the house boarded , the criminality decrease :
for chester city we obtain 3888 violent crimes per 100k that is -0.7%
for the mean in USA the value decrease by 0.5% at 1168 violent crimes
Employment
Is increasing employment a solution ?
The employment permits a more great stability on a family.
if we increase the employment by 10%, the criminality increase:
for chester city we obtain 3946 violent crimes per 100k that is +0.8%
for the mean in USA the value decrease by 5,3% at 1235 violent crimes
It s an interesting point to save . How to explain if we increase the employment the criminality increase. The model don t explain some relation between the employment and the violent crimes.
Mathematical approach
I have an other approach , more mathematic. For example we can search some extrema in our model.
I start in reviewing the linear model and finish in testing each value independently.
Best regression values
There is two extrema value in our model. A positive value that if we decrease it should decrease the violent crimes and a negative value that inversely do the same thing.
For the positive value, the name is OwnOccMedVal that corresponds to the owner occupied housing on median value. If we decrease this value on all county or in cherster city the result decrease.
For chester city the criminality decrease to 3902 (-0.35 %)
For the mean in USA the value is important and decrease to 1084 violent crimes by 100k hab (-7,60%).
For the negative value, the name is numbUrban that corresponds to the number of people living in areas classified as urban.
If we increase this value by 10 pourcents, the violent crimes decrease on chester city and in the USA.
for chester city the value decrease to 3895 violent crimes (-0,52%) that is better that the previous value.
fro the mean in the USA the result is 1155 violent crimes and decreasing by 1,60%.
Testing each variables independently
Testing value by value is more mechanic but permit to search between the model on the data which value to change to obtain a best result.
I have tested in increasing each value by 10 %
I obtain two values , one for chester city and one for the mean in the USA.
Fo chester city the value is PctLargHouseOccup corresponding to the percent of all occupied households that are large (6 or more people). We decrease the criminality to 3786 crimes (-3,31%).
For the mean of the USA is PctPersOwnOccup corresponding to the percent of people in owner occupied households. We decrease to the principal value to 1016 (-13,43%) .
In testing in decreasing each value by 10% we obtain different value for chester and the mean value in the USA.
For chester city the more impacting variable is racepctblack correspond to the pourcent of black people in the city. In chester city the number is more than 74%. In decreasing this value by 10 pourcent we can decrease the violent crimes to 3757 (-4,05%).
For the mean in the USA the value is PersPerOccupHous corresponding to the mean persons per household. In decreasing this value by 10% the result is decreasing to 1048 (-10,72%).
Conclusion
It s difficult to give a valid conclusion on the fact that the most important variables that is apt to decrease the violent crimes are based on house. It ' s easy to imagine that in giving more possibility to be owner of their house we can improve the socio economic environment . But to enhance this need , the population have to get a better economic situation that is not the case at Chester city.
In chester city, reducing black concentration and increasing the house size could affect durably the criminality. It s not impossible to politics to take action on this parameters.
