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Network flow model using temporal GIS


Case Study
The case study is taken for the educational Institute, ‘Birla Institute of Technology and Science, Pilani’ (BITS). The situation is simulated by allotting the number of students from the different hostels (supply points) to different course sections (demand points) at different time. This objective is to allocate the different course sections to students from different hostels at different time. The same course is taught by different instructors at different time and hence there are number of sections for the same course. The constraints are applied in terms of number of students per section. The BITS campus has eleven boys hostels and one girl hostel. There are three faculty divisions (FD) where the classes are conducted. Each FD has two floors and there are approximately 50 classrooms in three FDs. The allotment is to be done on the basis of travel distance from the hostels to the classrooms. The classroom with lower travel distance has been given the higher priority. Moreover the allotment is not on the basis of students in individual; rather it is done for the total number of students as an input from a particular hostel. The steps are as follows:
  • First of all the image of BITS campus has been scanned and imported to the IDRISI environment. After that the FD map has been prepared over the excel spreadsheet (Fig. 2). Excel worksheet was chosen for this task as it provides the facility of representing a classroom using the range object.
  • Non-spatial data is entered using the MS Access module. The data related to the classrooms, hostels, courses, distances, time, etc. is collected and a database is prepared in normalised form.
  • BITS campus map was digitised in three modes i.e. point, line & polygon: (i) Point type: In this type all the important landmarks are set over the map, (ii) Line type: The roads, connecting the landmarks, of the campus were digitised and vector image was obtained, and (iii) Polygon type: To represent the built environments of the campus. This includes the FD, hostels, temple & lawns. FD map was also digitised in form of ground floor and first floor for classrooms.
  • These vector layers of BITS campus and the FDs were rasterised, so that, they can be linked to the database. For this, the point type, line type or the polygon type vector layer is given as input image for the rasterising function of the IDRISI software. This updates the original raster image by the vector layers.
  • Two databases, one for non-spatial and second for spatial information, were created. In all six non-spatial tables were prepared. These refer to the classrooms in each FD at each floor. So, three FDs and two floors in each FDs result in six tables. These tables consist of the data related to field ‘di (internal distance factor)’ and the field ‘strength (capacity of each classroom)’. Secondly, the table to be prepared is related to the hostels. This consists of fields as ‘no_stud i’(number of students in ith year) and three other fields which keep the information about the distance of the hostel building from each FD. Another table ‘courseinfo’ contains the fields as ‘course_id (unique course number)’, ‘level (students of which year)’. Another table ‘ref-text’ contains the fields as ‘hr_no (which hour of the day, i.e. 1,2,…,9)’, ‘days’ (Monday, Tuesday, etc), ‘room_no’ (the room, where the class is conducted). There are a few tables prepared to contain the data regarding the vector objects created. Tables ‘roads’ (containing information about the roads), ‘class-first’ and ‘class-second’ (data regarding classrooms) and ‘hostel’.
  • Writing macros: This part is the backbone of the project. This refers to programming the range objects, which further represent the Faculty Division (FD) plan over the spreadsheet. The in built Visual Basic environment allows the facility to customize the macros which do the task using Eq. 1 The parameters are used to find out the travel distance. FD distance can be searched from the hostel table. Internal distance factor can be accessed from the classroom tables. Class hour can be accessed from the course section table. VB SQL is used to perform the search operations.

    Hostel name, year, course name is taken as input from the user. After that, the effective distance is calculated and tables are updated. Once the effective distance is calculated, priority with respect to each section is determined. Section with lower travel distance gets the higher priority. After this section allotment is done to the students. Now these rooms are shown over the FD plan on MS Excel spreadsheet (Fig. 3).
Table 1 and 2 show the results of the case studies in terms of allotment of students from the different hostels to different sections for a course at different time.



Conclusion
The model can be applied to real life problems, for example, in a city to allot the time slots at public service centers. However, the criterion for finding out travel distance will be different. It can be in individual or can be specific to a particular zone. On the basis of the information, prioritisation of the facility centres and allotment can be done. The temporal consideration has to be taken keeping the peak traffic hours. The work emphasises the interfacing of GIS with other software packages, which can improve the efficiency of existing GIS packages.

Reference
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