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Frogtown, MN, Crime Map 01/29/20

By Frogtown Crusader (Abu Nayeem)

Purpose

As a Frogtown resident, it is important that residents are aware on what is happening around our community and how we address it. Every year, there is commotion about safety. Some community members feel that the community is getting worse, while others are saying its getting better. While the city and police department might be saying something totally different. In our current political climate, it's controversial to talk about crime given it's strong correlation with poverty. Unfortunately, this can disenfranchise some community members whom are victims of crime and live in heavy crime areas on a daily basis. These reports and graphs are met to just shed light on the issue and encourage community members to see and interact with the data themselves. Hopefully, the community and/or agencies will take action. Note: Parts of Midway, Rondo, and Union City are included.

Some questions that you may consider:

  • What are vulnerable areas or hotspots in the community?
  • Are certain crimes more frequent in your area, and how should you or the community address it?
  • What are some trends in the your community?
  • What is the frequency of calling the police in nearby area? Is there over-reporting/ under-reporting?
  • How can we address these issues as a community (i.e. organizing; etc)?

Open Source Data Initiative

It is important that data is accessible and provided to the public. This report and others will be available on Github allowing others to contribute, replicate, and use code for their own respective neighborhood. If anyone is interested in mapping out East Side, Payne Phalen, etc., please reach out to me.

You can use the data provided by this report, but understand that I'm not an official agency and not liable for incorrect data.

About the Dataset:

The Crime Incident Report - Dataset was obtained from the Saint Paul Website. It is publicly available. The report contains incidents from Aug 14 2014 through the most recent date, as released by the Saint Paul Police Department.

A few notes about the dataset:

  • The dataset have several human errors such as mis-categorizing addresses and address being designated to the wrong police grid.
  • The dataset DOES NOT PROVIDE EXACT ADDRESSES/ GEO-COORDINATES. However, I've constructed an algorithm that get a reliable proxy address for most entries.
    • The process included entering coordinate manually; if any residents are interested in mapping out their own community please feel free to connect with me.
    • Google Maps geocoder was used
    • The algorithm does not cover the intersections of Capitol Heights and Mt. Airy region. Some data was excluded from region due to inadequate mapping. They have many intersections and curved streets; it would take time to geo-code it.
  • The analysis/algorithm is restricted near the Frogtown area.

Saint Paul Police grid w/ total crime numbers all years [excluding proactive visits]

The dataset comprise this area.

title

Display Total Incidents of all years

NOTE: The following changes were made in the crime category to consolidate categories

  • Graffiti was combined into Vandalism
  • Violent Crimes combined the categories: Rape, Homicide, Aggressive Assault
  • Domestic Assault includes both Simple and Aggressive Assaults
  • Community Engagement Events and Proactive Police Visit are not crimes. Proactive Police Visit is not response to 911 call. They will be excluded for vast majority of the analysis
In [42]:
print('List of Events in Frogtown (nearby) from 2019 to Present')
fg.query('Year>=2019')['Incident'].value_counts()
List of Events in Frogtown (nearby) from 2019 to Present
Out[42]:
Proactive Police Visit        4417
Theft                         1396
Vandalism                      418
Auto Theft                     401
Narcotics                      281
Domestic Assault               226
Discharge                      225
Burglary                       223
Community Engagement Event     128
Robbery                        101
Violent                         86
Arson                           14
Name: Incident, dtype: int64

Frogtown Yearly Crime Comparison Up to Current Date

How much crime is there so far in comparison to the previous year on this date? Are certain areas increasing or decreasing? Note: the function can choose any day prior to the current date.

We choose the max date for the dataset. We can choose an earlier date if desired

In [22]:
plot_toDate_Year_Crime(Incident='All',Day=Max)
This graph maps All incidents up to 1/29/20XX

Frogtown Total Crimes toDate by Month

What are some monthly trends

In [23]:
table_toDate_Month_Crime(Incident='All',Day=Max)
This table maps All incidents up to Day 1/29/20XX
Out[23]:
Month 1
Year 2018 2019 2020
Community Grid
Midway 66.0 1 0 0
86.0 25 14 14
Summit-University 107.0 17 24 14
108.0 10 23 19
109.0 8 3 8
110.0 19 17 11
Thomas-Frogtown 67.0 9 6 9
68.0 4 3 3
87.0 24 19 23
88.0 18 26 25
89.0 47 31 41
90.0 31 15 20
91.0 6 3 3
92.0 0 0 1
Union Park 106.0 32 35 49

Saint Paul Yearly Crime Comparison Up to Current Date

How do we compare to our neighbors and what are some trends?

In [20]:
plot_toDate_Year_SPCrime(Incident='All',Day=Max)
---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-20-ba2613de9668> in <module>
----> 1 plot_toDate_Year_SPCrime(Incident='All',Day=Max)

NameError: name 'plot_toDate_Year_SPCrime' is not defined

Frogtown Annual Crime Map (Default 2020)

At least four crimes must occur in one location to be displayed

NOTE: It's interactive, click on the circles for more details on the total number and type of crimes committed

In [21]:
plot_Frogtown_year(2020)
Out[21]:

Frogtown Violent Crimes Map from 2018 to Present

Violent crimes are less salient or apparent to community members. We can see which areas/ blocks are safer.

  • Orange= 2018
  • Green= 2019
  • Purple= 2020
In [22]:
plot_Frogtown_long_crime('Violent')
Out[22]:

Frogtown 2020 Multi-Crime Map

  • Purple= Discharge
  • Green= Autotheft
  • Orange= Theft
  • Blue= Burglary
  • Brown= Narcotics
  • Red= Vandalism
In [28]:
plot_multicrime_byYear(2020)
Out[28]:

Hotspots near Frogtown from 2019 to present

Notice the address codes are masked and count is limited to 15

In [30]:
B=fgc.query('Year in [2019,2020]')
B=B[['Block','Count']].groupby(['Block']).sum().reset_index()
B.columns=['Block_Intersection','Count']
B.query('Count>14').sort_values(['Count'], ascending=False)
Out[30]:
Block_Intersection Count
143 130x university av w 490
114 123x university av w 54
200 27x lexington pa n 53
491 62x rice st 48
271 39x lexington pa n 38
844 95x lexington pa n 33
161 17x charles av 26
971 hamline_university 25
120 124x stanthony av 23
642 75x milton st n 23
929 dale_university 22
376 50x rice st 20
872 98x university av w 20
343 46x dale st n 19
51 107x university av w 18
163 19x edmund av 16
483 62x aurora av 15

Frogtown Hotspot Interactive map from 2019 to present

  • The bubbles are not to scale across the different categories to make this map more accessible. You may need to zoom in to distinguish points
  • The graph is Interactive; if you click on a point, it will list the number and type of incidents in that area
  • Legend
    • Orange: Number of Incidents greater than 10 and less than 20
    • Green: Number of Incidents greater than 20 and less than 50
    • Purple: Number of Incident greater than 50
In [31]:
plot_Frogtown_hot_spot()
Out[31]:

Frogtown Gun Discharge Reports

We hear it all the time from many people that our awareness of shootings is based on connection to social media and officials suggesting it is safer now than years prior. Is this true? Well, let's find out. It's worth noting that weather conditions can influence the frequency of when shootings occur. Finally, the discharge category excludes a firearm being used to assist in another crime.

Why focus on shootings?

Shootings can harm innocent bystanders and creates a significant sense of fear because it can be heard from a distance. It is one of the more salient crimes.

First let's construct graph a table showing the total number of shootings up to current date in comparison to previous years. Notice that many grids are experiencing different trends, so it can be true that some neighbors are exposed to more shootings this year and others less.

In [32]:
plot_toDate_Year_Crime(Incident='Discharge',Day=Max)
This graph maps Discharge incidents up to 1/29/20XX

What are some trends at the city-wide level?

In [33]:
plot_toDate_Year_SPCrime(Incident='Discharge',Day=Max)
This graph maps Discharge incidents of Saint Paul neighborhoods up to 1/29/20XX

The table below displays the number of shootings by grid in Frogtown broken by month over the last two years

In [34]:
table_toDate_Month_Crime(Incident='Discharge',Day=Max)
This table maps Discharge incidents up to Day 1/29/20XX
Out[34]:
Month 1
Year 2018 2019 2020
Community Grid
Midway 86.0 0 1 1
Summit-University 107.0 1 2 3
108.0 0 2 5
110.0 1 0 1
Thomas-Frogtown 68.0 1 0 1
87.0 1 3 3
88.0 2 3 3
89.0 6 1 9
Union Park 106.0 1 0 0

Frogtown UptoDate gun discharge map 2020 to Present (interactive)

A police Grid can cover a lot of area. From the map we can spot parts of the community that are currently vulnerable and those in the past.

  • Orange= 2018
  • Green= 2019
  • Purple= 2020
In [37]:
plot_Frogtown_long_crime_todate(Incident='Discharge',Day=Max)
This map displays Discharge incidents up to 1/29/20XX
Out[37]:

Frogtown UptoDate Daytime Discharge Map

Daytime shooting are particularly concerning to our community as there as there are greater frequency of innocent bystanders being harmed and can influence community members behavior to go outside or parents decision to let their kids play.

  • Orange= 2018
  • Green= 2019
  • Purple= 2020
In [36]:
plot_Frogtown_long_crime_daytime_todate(Incident='Discharge',Day=Max)
This map displays Discharge incidents up to 1/29/20XX from 7AM to 8PM
Out[36]:

Frogtown yearly gun discharge from 2018 to Present

  • Orange= 2018
  • Green= 2019
  • Purple= 2020
In [19]:
plot_Frogtown_long_crime(Incident='Discharge')
Out[19]: