City Planning

      It’s been estimated that more than 50% of the world’s population lives in cities nowadays. It is also been observed that, in the next twenty years, another 2 billion people are expected to move to urban areas which are already overcrowded. The pressures of rapid urbanization make the careful urban planning more difficult. What’s more, for example in China, with the development of the urban area, more and more cities plan to build more advanced public traffic system. Thus, to help urban designers and urban planners to make better decisions of building greater urban area and design better route for public traffic lines, it is a nice option to use UCINET and NETDRAW software to do the urban planning.

     For instance, a better subway system is the one that effectively connects different places people go frequently. It should help residents and travelers get wherever they want in a most effective way. To use the software, we can firstly design a survey providing a list of places (tourist attractions, residential district, commercial area, etc.) to all kinds of people (residents and also travelers) in the city, say QINGDAO (a northern city in China, who is building subway lines in recent and upcoming years). Ask each person who takes the survey to use a five-point scale to evaluate the rate of the frequency they go to those places.

        1 = Never

        2 = Seldom  

        3 = Generally

        4 = Often

        5 = Always

       The purpose of the survey is to determine the most effective and convenient route of the subway lines. The number associated with each places are recorded in the UCINET software. We can dichotomize the data to narrow the span of places who received rating of 4 and 5. In other words, places that people go and visit most frequently. In such survey, we can also ask the survey takers to characterize the attributes (what kinds of the area, what services around such place) of the places where they barely or always go. Besides, we need to characterize the survey takers (their genders, professions, even hobbies, etc.). We name those places that people always visit as the “star places”.

        After receiving the data from the survey, we can also do the social network analysis by addressing the following questions:

·         To identify those “star places”?

·         To identify the places that are not well connected to the others.

·         What is the density of different places, and how can it be enhanced?

·         Identify those places which are not “star places” but are well connected between different “star places”

·         What are the key attributes of the place that people most frequently go to, it is because they live or work there or they like doing shopping there?

·         To identify what’s the emerging “star places”.

·         To classify different “star places” into different subgroups.

By using the results of the survey, we will be able to know centrality measures of which place has the highest eigenvector and in-degree number, also the smallest closeness number. These numbers will provide results of which places are the most popular.

After getting these results, we can build up the subway lines according to the results. Following thoughts should be taken into account.

l  The subway lines should cover all the “star places”.

l  The density and the shape of the subway lines in the system.

l  Each line should conclude almost all kinds of services.

l  The intersection point of different lines should be at the “star places”.

l  Each line should be well-balanced which means it needs to conclude the bustling city lots and the lots that are not bustling enough, in order to make all areas develop at a same pace.

In a word, using the social network analysis, it will be easier to do the city planning because such method provides a way to identify the “star places” (for example, the nodes with higher degree) and also a way to well connect these nodes (by analyzing the betweenness, closeness, and also the connection of subgroups).

                

                                                                             -by Longfei CHENG, 2014 HULT MIB