2.1

Crime Mapping

Numerous studies have been introduced to investigate the solution of criminal behavior in modern society^5,6. Crime mapping is an essential requirement to build a safer community. With the crime maps, the authority could prepare the appropriate preventive action to improve the safety of each area. Crime maps could records of crime incidents as well from OpenStreetMaps¹⁷. Brantingham and Brantingham⁷ introduced the Location Quotients, Counts, and Rates as the parameters to analyze and map the high-crime area in British Columbia. The variable “Counts” and “Rates” are common in the conventional crime report of a specific location. However, the variable “Location Quotients” give a pattern perspective of crime activity in a specified location. Therefore, each area will have a different characteristic of crime risk. Visualization is essential to make the crime risk characteristic of a region more comfortable to understand. Domino Project⁸ used Python programming to maps the crime-prone area in San Francisco. This project showcased the use of Folium Library in Python to create map visualization. Kahle and Wickham⁹ used the R programming language to create a contour map of the high-crime region in Houston. They combine ggplot2 and Google Maps to create spatial visualization using R language.

2.2

Online Map and Navigation Application

In the current digital era, the development of map and navigation applications have become inevitably popular. In early 2000, White et al ¹⁰ develop a map matching algorithm for the 3rd generation Personal Navigation Assistant (PNA). The map matching algorithm searches the location/coordinate that is inputted by the user in the prepared maps’ database. The development of map and navigation application at that time is far less sophisticated compared to the current system.

Steinmann et al¹¹ brought up public participation in online maps development. In 2005, the two ways of communication between online application developers and the users were becoming possible to exist. However, there was no available technology that readily available to support that function. In 2013, Hu & Dai¹³ discussed the fast development of online map applications. Spatial data from many sources have been integrated to provide more specific service. It could be observed from the usage of Application Programming Interface (API) to make the application became more intuitive and gave an option for the user to input data to the application. Those input data through API were recorded and processed to develop and update the online maps.

3.1

Route Calculation

The route calculation method is the principal element of the route determination in any navigation applications.The route calculation method computes the original coordinates as the first location and destination as the second location using search function in the road network. The search function involves various decisions such as the distance of each segment that must be passed, signs, and boundaries associated with each segment. With specific algorithms, the optimum route is then selected. In general, the reference in determining the optimum route is travel time. However, the calculation of travel routes also provides a variety of alternative routes for users of map and navigation applications¹².

Contour Map is a description of a function that has 2-dimensional input with 1-dimensional output¹³. For example a function fx, y = 3x² + 3y² produce a 3-dimensional graph of a paraboloid, as shown in Figure 1 and Figure 2. Each connected line is called a contour line, and each contour line represents a value. Each point in a contour line has the same value. Application of contour maps in crime set a value that represents the crime rate at each point representing an administrative area. The assessment can be in the form of the accumulated intensity of crime cases accompanied by the weight of each case points with the same level of a crime connected to one line. Points between the two crime rate values are interpolated to get an estimate of the value of crime rates in the area.

Figure 1.

Paraboloid¹³

Figure 2.

Contour Map Function¹³ f (x, y)

3.2

Comparison of Existing Crime Avoiding Methods

At present, two methods are widely known in the existing map and navigation applications that can be used to avoid crime-prone areas. Those methods are called as Rerouting and Contour Map, as shown in Table 3. With the rerouting method, users will get the road route recommendations, along with estimated time based on map and navigation applications. The next step, users change the route to avoid areas prone to crime. This method provides flexibility for users to choose the path. However, this method reduces the efficiency of the path recommendations offered by the application. The second method is the Contour Map Crime. Some administrative regions have maps of crime vulnerability. The map can provide information about the area that needs to avoid. However, the map has a weakness in the form of generalization of the cumulative crime in a region. However, both of those methods, despite their useful properties, are still lack of providing automatic recommendation to ease the user when interacting with the application regarding the concern of safety route issue.

Table 3.

Comparison of Each Methods

3.3

Proposed Framework

Instead of limiting our framework to the existing methods, we propose to provide automatic route recommendation based by blocking the potentially crime hotspot area. To determine the crime hotspot area, we refer to the historical crime data from the local government agency, existing geotagged social media crime news and user reported data. DKI Jakarta Provincial Government published the crime and assault record data¹⁴. In this dataset, each historical crime data consist of the following information:

The social media such as Twitter and Instagram also provide a geo tagging feature that is also commonly use to report the incident case, including the crime activity. The geo tagging crime report using social media is accessible by public and become one of the source of information. This application also provide a platform for the users to further involve and contribute by reporting their personal safety experience to increase the recommendation accuracy in the future. Based on these data, crime hotspot area is mapped as a contour map. The accident coordinate of each crime per type are counted and mapped as crime-prone area contour map. The map shows the crime level of the region from a certain type of crime activity.

Build on the free and opensource GraphHopper Routing API¹⁵, our proposed framework uses the route selection mechanism based on the blocking and unblocking feature. The blocking feature will put the crime hotspot coordinate as restricted point and notify the route selection algorithm to avoid that area. Our proposed personalized user feature empower the users inputted information such as reporting a new crime based on their personal experiences. The detailed propose use case diagram of user involvement is shown in Figure 3. We focus on Jakarta area as our case study, which served as the heart of citizen activity in Indonesia.

Figure 3.

Proposed Use Case Diagram of User Involvement in Our Framework

Algorithm 1 illustrates the automatic route selection based on crime area blocking. The historical crime data and user reported information are stored in the database which further being used to assign the crime hotspot area. The area will then be automatically avoided under blocking feature. The user will receive the estimated travelling time and the recommended route which minimize the path through crime-prone locations.

Algorithm 1.

Automatic Route Selection based on Crime Area Blocking

4.1

Statistics of Crime Historical Data

We use the crime hotspot area based on crime history from the local government agency, existing geotagged social medeported data. The users could further involve and contribute by reporting their personal safety experience to increase the recommendation accuracy in the future. The view of crime historical data are only accesible and editable by administrator. The following are the user interface of the proposed framework.

As can be seen In Figure 4, there were coordinate1 and coordinate2 as located crime prone areas. After get validation from administrator, we will know about crime prone areas and it is automatically finding route to avoid crime prone areas. The results is recommended routes blocking area.

Figure 4.

Example of the crime historical data and user reported data

4.2

Regular Routes without Area Blocking Feature

By default, the user are able to determine whether the crime hotspot are blocking feature will be used or not. If the blocking feature is not selected by user, our framework will display the regular route based on the fastest estimated travelling time. User will receive the route and its detailed step-by-step navigation equipped with the estimated distance and travelling time for each step as shown in Figure 5.

Figure 5.

Example of generated regular routes without blocking

4.3

Proposed Routes with Blocking Area Prone to Crime

Based on the available historical crime data and user reported experience recorded in the database, our framework will provide the automatic safety routes which avoid crime hotspot area. Users are also able to select the travelling choices: by driving a car or by walking.

Figure 6.

Example of recommended routes with blocking crime-prone areas

With this method the user will get a safe experiences by recommended routes with blocking crime prone areas. In the others result, By choose blocking features showed a long route recommendation than regular route because automatically avoid the crime prone areas. It makes takes time on the road but have a safely trip.