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From "Nadeem Anjum (JIRA)" <>
Subject [jira] [Commented] (SIS-97) [GSoC] Agent Based Modeling based geo-profiling of criminology projects
Date Thu, 02 May 2013 06:34:15 GMT


Nadeem Anjum commented on SIS-97:

Hi All,

I am Nadeem Anjum, a third year bachelor's student of the Department of Computer Science and
Engineering, Indian Institute of Technology (IIT) Kharagpur.  I am well versed in  Java, XML,
PHP, MySQL, JavaScript, jQuery, HTML, CSS, C, C++ and Python. 
I have been an active contributor to numerous development and open-source projects:
I have also been involved in a number of research projects:

I have been in touch Prof. Suresh Marru regarding the GSOC project:

I have done the required background reading for the proposal and want to share my ideas for
your feedback.


Embedding Google Maps and randomizing agent moves: We shall use google maps API and the location
of an agent will be denoted by the latitude-longitude. The agent will be able to move along
the roads on the maps only. The houses/buildings will be the sites the criminal agent (c-agent)
can break in. Each house/building will be associated with a victim-agent (v-agent).

Randomizing agent moves: The c-agent will have a predefined starting position (lat-long).
Along a single straight road, the agent can choose to move in either direction with a random
probability of 0.5 each. Across a road junction having x roads, the agent can choose to move
along any road with a random probability of 1/x. 

Simulating Distance Decay Theory:
The agent starts with an initial equity and is deducted a mark for every y meters (say 100
meters) the agent travels. This ensures that the agent does not go too far away from its home
location. Each house has a profit value associated with it and a level of acquaintance of
the v-agent with a c-agent. On coming across each house, the agent decides with a random probability
based on the above two factors, whether or not to break into the house.
On a successful break-in, the agent's equity will be increases by the profit amount of the
house and the agent returns to his home.

The agent is designed to return to his home following the shortest distance from any location.
The program will be designed to ensure that the agent will decide to go back, so that it is
able to reach home without spending all its money. To ensure that the agent does not go on
a crime spree, we make the agent turn back immediately after breaking in a cell and put a
maximum amount that can be spend in travelling (say around 10% of the initial equity).

Memory Function: The c-agent will refrain from committing crime in its immediate neighborhood,
i.e. with the v-agents it is acquainted with. We shall define a circular area of say z meters
square (say 100 meters square) and the c-agent shall recognize all v-agents falling into this
circle and vice-versa. However, we also need to take into account that the agent forgets those
who he has not met for a long time. So if the v-agent passes through a c-agent at time T0,
at time Tn, the probability that the c-agent will recognize the v-agent and vice-versa will
be 1-f(n), where f(n) increases wrt n. Say f(n) = 0.01*n, where n is the distance travelled
in meters (assuming agent moves at constant speed of 1 m/s). This means that after travelling
say 50 m, the probability that the c-agent recognizes the v-agent is 50%, and the memory will
be active upto 100 m .
We also need to take into account the fact that if the agent sees a person repeatedly, it
will remain in memory for a longer time. For this, we modify f(n) as f(x,n)=x*n. Initially
x=0.01. This means memory is active for 100m. If within 100m, the c-agent again interacts
with the v-agent, we change x to 0.9*x. This will ensure that the criminal will be remembered
more than 100m (long-term memory)

The agent will break into the house with a probability which is:
1. directly proportional to the profit value of the house
2. indirectly proportional to the probability that the v-agent recognizes the c-agent.

If the resident v-agent has the c-agent in its long-term memory, the c-agent is caught, its
equity drops below a certain threshold and the program restarts again. We shall run this simulation
a large number of times.

Simulating Routine Activity Theory: We can associate with each house/cell a fraction p, which
denotes the ease of breaking into the house. For example for a well-secured house with security
cameras, lights, fences, strong locks etc., the ease of breaking in will be very low. On the
other hand for an open park, the ease of breaking in will be high.

The c-agent will now choose to break into a cell with a probability which is:
1. directly proportional to the profit value of the house
2. indirectly proportional to the probability that the v-agent recognizes the c-agent.
3. directly proportional to the ease of breaking into the cell, p

Please provide your suggestions and feedback before I formalize my proposal.

Nadeem Anjum
> [GSoC] Agent Based Modeling based geo-profiling of criminology projects
> -----------------------------------------------------------------------
>                 Key: SIS-97
>                 URL:
>             Project: Spatial Information Systems
>          Issue Type: New Feature
>            Reporter: Suresh Marru
>              Labels: gsoc2013, mentor
> This idea targeted for GSoC project to extend the geo-profiling computational criminology
projects discussed in [1], [2]. Paper [2] uses a simple 2 dimensional spacial grid with x,
y co-ordinates to move the agents. This project should instead use google maps api to make
the agents move around as we validate various criminology theories like Distance Decay and
Routine Activity [3]. In the future these could be modeling in SIS when rendering is fully
> The project involves engaging with the SIS community for special expertise, understanding
google maps api, understand the basic concepts of agent based modeling using a simple proof
of concept like example [4]. The project deliverables includes a web based interface embedding
google maps and randomizing agent moves and develop a memory function and overlay mocked crime
data and predict crime patterns. 
> In addition to SIS PMC, external backup mentors Suresh Marru, Ramyaa Ramyaa and Arvind
Verma will provide the necessary guidance on the theory of computing  and logic, agent based
modeling and criminology concepts. 
> This projects is suited for students who can interpret computer theory and logic and
efficiently implement algorithms, to prototype with GIS based maps and have enthusiasm to
publish multi-disciplinary conference papers. 
> [1] -
> [2] -
> [3] -
> [4] -

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