A Case Study In The City of Lincoln

Heat map for Lincoln city

The structure of the observation method for this case study is the same as the observation method for UoL. However, as shown in Figure 3, the gate locations (in this case) are located differently from the University of Lincoln gates. The reason for this change is that the observer focuses on discovering the actual movement fellow in the neighbourhoods around the campus in the city of Lincoln.

The over results observation for the whole one year in the city of Lincoln shows the city has the same stable pattern of movement in a year (figure 3). From the August 2016 to May 2017, the amount of the people is changed by the different seasons of different time of the year, but the pattern of movement is similar. For the understanding of the layout of the city of Lincoln, please refer to figure 4. As figure 3 illustrates in all the months, the less movement in gates are related to the pathway near to Brayford pool and on the gate near to high street (the fewer movement appearances on the heat map by the range of colour between white to yellow) (refxxxx). On the other hand, most of the movements are related to the High Street area. It is essential to mention that, 2 KM around the university in the city is the highest movement spaces in this city, and this is the reason that the results are showing large amounts of people flews in all the gates.

With looking close for each month’s movements, the figure 3, illustrates that the highest movement is related to the December. During December, the city of Lincoln has several festivals and events. As well as, the Christmas markets in Lincoln is one of the potential factors for bringing people into the city. However, the less movement in a year belongs to the April. Which again it is important to mention that, except December, the numbers of people flew between the other three months are nearly the same. This result shows that the inside of Lincoln, specifically the area around the university, has the customary movements. As the observation analysis for the University of Lincoln shows, the pattern between the city of Lincoln and the University of Lincoln are the same in different months.

Let’s start by looking each month individually, as the figure 3 shows in August; the less movement is related to the area behind the Mark and Spencer shop. This area is a pedestrian alleyway which directed people to the High Street from the Brayford pool. The second less area is shows between the in Brayford Wharf N and the Fosse Bank. Although the rest of the city shows almost same amounts of movement, still the High street movement is the highest one. This pattern of the people flew slightly shifted in December 2016. The fewer movements in December is related to the Foss Bank area. The reason for that changing between the less movement in August and December connects to the number of tourists. In December, the number of tourists is higher than the August. Plus, the Foss Bank area is the residential area which means the only local people use that area (figure 4). The tourists use the places that have more retail area including High street. The second less movement are in the Brayford Wharf N and the area behind the Mark and Spencer shop. However, the rest of the gates in December shows the high moves in the city. Anyhow still, the High Street shows the highest movement in the city. The patterns of movement in April 2017 and May 2017 show the same results from December pattern. The only difference is about the amounts of movements. In April, the number of people flew in the city is less than the rest of the months. By mentioning that the city of Lincoln is one of the university cities, these changes are understandable. In April, the people, specificity the students, leave the Lincoln because of the Easter time, and this can affect the number of movements in the observation results. Furthermore, in May, this number goes up which again is more related to students.

In overall, the pattern of people flow in the city of Lincoln is the same in the whole year (figure 3). The High Street pointed as the highest movement in this case study and less movement referred to the Foss Bank area. By looking for the heat maps for both the University of Lincoln and city of Lincoln, it is clear that rise/ fall of the movements, it has happened in the same months for both cases. This again highlights that have the influential physical connectivity between the city and the university. Taken as a whole, the pattern of movements in the area around the university inside the city of Lincoln demonstrated that have the well- structured physical accessibility between the town and university. In this case, the physical access between the city and university might not issue between the city and university campus, but with consideration to as the early discussion in Heatmap for the University of Lincoln, the University of Lincoln need to improve the quality of access to the campus.

5.3 Analysis and Discussion

In this section, the result for axial analysis and observation modelling will be analysed for understanding the relationship between the physical integration and actual movement in case studies.

5.3.1 Compare the Depth map modelling and observation modelling for Lincoln city and university of Lincoln

Comparing the results between the Space Syntax integrations and the gate count observations for the University of Lincoln, it is apparent that the most integrated spaces in the university are the spaces which exhibit most of the movements. For example, in global integration, the most integrated area is related to the pathway that connects the Main building to the Campus Way area, which is also the same place in the heat map where the median amount of movement occurs. Despite the correlation between the gate count movements and the axial line measures, it is important to note that, the heat map was most pronounced in an area that had no correlation to the axial maps. The result between the observation and axial map demonstrate that in certain areas of the campus, space is not always utilised in the way that the campus designers intended. (write how is important people shaping the city)

The results of the integration analysis and gate count observations for the city of Lincoln illustrated the correlation between the highest movement and the highest integrated spaces in the city. For example, the High Street has the most integrated space in the town, which also has the highest movement. The correlation between the heat map results and the Depth map analysis results suggest that have the strong connection between the physical accessibility and human behaviours inside the city. Furthermore, with consideration of the research question, these results show it has strong physical accessibility between the University of Lincoln campus and their surrounding area. Moreover, in terms of the physical connectivity, the University of Lincoln merged within the city. However, in this specific case study, it is essential to mention, the gate count observation is not covering all the places inside the city. The reason for that is related to the limitation of the study, which will be explained in the last chapter.

5.3.3 Compare the Depth map modelling and observation modelling Bath city and university of Bath

For comparing the global and local integration results, it is clear that in both scales, the University of Bath has a strong correlation between the spaces inside the campus. In the local and global integration, the routs around the edges of campus have the less integrated rather than the other part of the system, and the rest part has the average to highly integrated. These analyses for the University of Bath, with consideration of the location of the site and the observation data, demonstrate that the campus is designed as one system which can work to gather utterly. The correlation between spaces and landscape of that campus make the place more integrated. However, this result does not mean that the campus does not have any potential for the future improvement. In fact, the University of Bath for having the stronger correlation in the campus has the master plan for development (Univeristy of Bath, 2009). In this case, as explained in chapter 4, the location of the university campus is far from the city centre, and this is a reason that the gates for observation mainly focused inside the University of Bath campus. However, data gathered for only one week for a few gates in the city of Bath, same as the city of Worcester, because of the limitation. It is essential to state that in this case, having more data could cause the changing the result.

5.4 Develop an analytical framework of the university impact on the city: QI scatter plot

This section will be developing the analytical framework for testing the research hypothesis. Chapter 4 and some parts of chapter 5 are looked at certain criteria to investigate the relationship between university and city. This section modelled non-physical aspect between university campus and city in two different categories. The first one is related to qualities of a university, which is named by University Performance Quality Indicators (UPQI). The UPQI includes different indexes such as National Students survey (NSS), Teaching Excellent Framework (TEF) and Research Excellent Framework (REF). The next category has referred to the qualities of a city that described as City Performance Quality Indicators (CPQI). The CPQI includes crime rate, salary, house price and life satisfaction. The next part of this section will discuss how the indexes are chosen. But before that, it needs to explain the methods for selecting the universities and the cities.

This research is on the universities in the United Kingdom. According to the Universities in the UK, there are 162 higher education institutions in the UK. This research selected 43 universities out of 162 with the Specific criteria. These criteria are as follows: First, universities inside the London are not included in this research, because the city of London is a "metropolitan city" and it has a different character than the other cities in the UK. The second is related to the cities that have more than one university. In this situation, the largest university in each city is selected. For example, the city of Bath hosted the University of Bath and the Bath Spa University, which the University of Bath nominated. The third one is more related to indexes in the UPQI. In this part, universities were selected that have all results in NSS, TEF and REF. Finally, the last part linked to the cities. Cities that have not published the indicators of CPQI were not selected.

The other issue that needs to consider in this part of the analysis is related to NSS and crime rate. The NSS survey opens for all the universities in the UK every year. In 2016 almost all the universities across the UK completed the survey, and they had NSS results. However, in 2017 few students’ union started to boycott the NSS lead by National Union of Students (Union, 2017). In a past few years, the government introduced some reforms components to Higher Education. The most important one is teaching excellent framework (TEF). As mentioned before, the TEF is divided by gold, silver and bronze to rank teaching at universities and One of the elements for this calculation is NSS data (Depatmantof Education, 2017). The NSS result for the final year students is includes some questions about their experience at the university. The government allows the universities that have gold and silver in TEF, to increase the tuition fees and this is the reason, some students’ union join the campion of NSS boycott (Union, 2017). The universities are joined for this campus includes university of Manchester, University of Oxford, University of Cambridge, University of Sheffield and University of Bristol. The results of this boycotting are that in 2017 and 2018 those universities did not appear in the UPQI list. Although this action is an effect on UPQI, this study decided to use the NSS data in 2016 in order to contain all the universities.

Regarding the issue of crime rate in CPQI, it is important to clarify that the data for the crime rate is not for the whole region in each city. Moreover, it is not including all the crimes. In this study, with consideration of the research questions, the crime rate data is limited to the crimes that often impact students such as burglary, violence, robbery and sexual offences in the University campus and their surrounding areas. Table 1 describes the variables and the source of data for each UPQI and CPQI indicators.

In this case, the UPQI used the standard variables, which applies for the national university ranking in the UK (Hiely-Rayner, 2016; The Complete University Guide, 2018; The Sunday Times, 2017). As mentioned in chapter 4, The Complete University Guide, the Guardian and the Times ranked universities with the different methodology with same indicators. With looking the relationship between the city and university the NSS, TEF and REF chosen for this research. Furthermore, this research is used the standard indicators of quality of life in cities in the EU. The quality of life in the city can be measured based on a variety of aspects that has impact on life such as economic aspects, environment, the social aspect, demography, education and culture (Hawksworth et al.,2017; Holgate, n.d). This study used the common variables of those indicators, which has the most impact on the relationship between university and city includes salary, house price, crime rate and life satisfaction.

QI scatter plot (Quality Indicator) illustrated the relationship between UPQI and CPQI (figure xxx). For understanding the method that used to generate the QI scatter plot, please refer to chapter three. Before continuing, it is important to say that the reason to create the QI scatter plot is to test the research hypothesis. Looking back to the results of quality indicators, in general, figure xxx shows that the relationship between UPQI and CPQI is fragile. For example, the University of Dundee is the high score in university quality but the low score in city quality. On the other hand, the University of Winchester is low in UPQI but in fact, has the highest score in CPQI. Also, some universities such as the University of Bristol has an average score in both UPQI and CPQI. Although the QI scatter plot demonstrated the weak relationship between university and city quality indicators, still p-value is 0.05684 which means that there is a relationship, but it is not strong enough to say the hypothesis of this research is valid. The research hypothesis stated that the "city/campus duality" which means that if the university success the city will be a success and vice versa. In chapter one, the hypothesis was defined. The figure xxx is highlighted some of the universities that have the relationship between UPQI and CPQI. All the universities (figure xxx) are in quadrant 1 have the high score in both university and city quality indicators which mean that the good university is in the good city and vice versa. On the other hand, the universities that show in quadrant three have the low score in UPQ and CPQI that repeat the relationship between the university and the city.

In order to understand more about the QI scatter plot, table 2 shows that, the percentile values for UPQI and CPQI in each university form the QI scatter plot. The top 10 universities of the table are related to the universities that have high in UPQI, but it does not mean that they have high in CPQI. For example, the University of Dundee rated as the 6th highest in the university performance quality in the QI scatter plot table, though the CPQI for the city of Dundee is only 9.302326 which categorised the city in the bottom of the list.
Nevertheless, among the top 10 universities in the table 2, six universities out of 10 have high percentile in both CPQI and UPQI that includes the University of Oxford, University of Cambridge, University of Exeter, University of Bath, University of Leeds and University of Surrey. From another point of view, four universities in the bottom of the list of UPQI have the high rate in CPQI includes the University of Winchester rated 100 in CPQI, the University of Chester rated 60.465116, Bournemouth University rated 69.767442, and the University of Brighton rated 83.72093. Although it seems that the relationship between the university and the city is not strong enough in this analysis, still the specific three case studies of this research show the strong correlation between the university and the city. The University of Bath, as the table 2 shows, has 90.697674 in UPQI and 93.023256 in CPQI which placed this university in the top 10 universities in the
table. In this case, the University of Bath has the similar good results in both variables in QI scatter plot that presents a relationship between city and university. On the other hand, the University of Worchester with having the 9.302326 in UPQI and 39.534884 in CPQI, listed at the bottom in the table of QI scatter plot table. Although the university performance quality for the University of Worcester is lower than the city, the university is classified as the weak relation in quality indicators between the university and city. Finally, the University of Lincoln is located in the middle of the list by 69.767442 in university performance quality and 51.162791 in city performance quality. In the view of this, the results of QI scatter plot table, allocated the University of Lincoln in the middle of the table. It means that the relationship between the university and the city is strong because both CPQI and UPQI are at the similar average rate.

Although the general view of this analysis highlights that there is not the strong relationship between universities indicators and cities indicators, the next section will show the actual existing pattern of the relationship between the cities and universities.

5.4.1 The impact of physical location of university campuses on UPQI and CPQI

The physical location of the campuses, according to Den Haijire in 2008, is categorised in three different categories: the integrated-city which campus situated in the middle of the city and integrated within a city, the gated campus that campus located in the city and surrounded by a gate, and the separated city that campus located out of a city (figure xxx). This research used these physical locations as a framework to analyse the UPQI and CPQI. First, all the case studies are divided into those three categories and then examine each physical category (table xxxx). In general, university campuses in the UK mostly are in either integrated-city or separated city, and just a few case studies appear as a gated inside the city. Figure xxxx shows the relationship between the university and city with considering the physical location of the campus. The correlation between UPQI and CPQI in integrated-city illustrates that the positive relationship between the city and university. However, as the figure xxx show this result is changed for the gated campuses. The results demonstrate the gated universities has a weak relationship between the campus indicators and city indicators, which can be understandable because of several reasons. One of the critical reasons is that although the campus located inside the city the university community is separated from the local community in the city and it because of the physical gate surround the university site. Separated city results show that, no relationship between the UPQI and CPQI and it because of the physical distance between the campus and city.

With looking for all three classifieds of the physical location of campus within the city to examine the relationship between CPQI and UPQI as one, the analysis shows that those universities that integrated with the city potentially correlate with the city. This correlation decreases for the campuses who they separated from the city. In the other word, the physical location of campuses has a significant impact on the relationship between the city and university. And it is not just the location. Also, it matters the campus form. As the figure xxx shows the gated universities, as a physical location is inside the city, but they have a negative correlation between university indicators and city indicators. This suggests that, the campus design also has a substantial impact on this relationship.

Summary

The major objective for this chapter has been concerned with the analysis of three cities and universities in order to understand the physical relationship between the university and the city, and also to generate the QI Bi-plot for testing the hypothesis. It is the beginning of this chapter, the theoretical review of spatial analysis (depth map modelling) was discussed. For understanding the relationship between university and city, the spatial investigation was applied for three case studies in both city scale and campus scale. In each case studies, the relationship was different according to the analysis, and the reason was more related to the physical location of the campuses within the city. For example, in city size, the University of Lincoln has the strong correlation between the city and university, and it because of two significant reasons. The first one is because the University of Lincoln located in the heart of the city and the second one, because of the campus design. The University of Lincoln is open access university which means that the site of the university does not have any gate and it is a welcome place for everyone to come. On the other hand, the University of Bath is far from the city centre, and the has a weak integration between the city and the university. However, in the campus scale, the University of Bath shows that strong correlation, which means that the physical location has a major impact on the relationship between the city and university. In fact, not the only the physical location of a university but a campus design has an effect on that relationship.

The next part of this chapter, in order to validate the spatial analysis in real life, the observation data adapted for analysing the actual pattern of movement in each case study. The observation data shows that the relationship between the university and the city is more related to the design of the campus. In the city of Worcester, although the university campus is located in the city, it seems that the design of the campus with the gate around it cause the reduces the number of people using that space, particularly those who they do not belong to the university. On the other hand, the University of Bath with having a natural gate still has a welcoming place for people who they want to use the space inside the campus, with consideration of the campus location is separated from the city. The observation data and spatial investigation illustrated the fact that the relationship between the university and the city is not only about the physical connectives, it is also related to the design of campus and how people using and shaping the space.

In the following section, the analytical framework of the university impact on the city has been developed in order to test the hypothesis of this research. In consideration of the relationship between a university and a city is correlated to the socio-economic, the UPQI and CPQI designed with the indicators that cover the main elements of this study. The results of QI bi-plot confirms that has a weak correlation between the UPQI and CPQI which means that the hypothesis is failed. It is important to mention that still, three case studies show that strong relationship between the university and the city. for understanding the reason of that, this study examined the result of QI bi-plot with the physical location of the campus within the city. The framework of the physical location of universities adapted from Den Haijir and adjusted with UPQI and CPQI. The results illustrate that has a positive relationship between the UPQI and CPQI in integrated-city campuses. As well as, the negative correlation between the UPQI and CPQI in gated campus inside the city. The results furthermore demonstrate that there is no relationship between city indicators and university indicators in the separated city campus. According to the finding in this chapter, the “city/campus duality” is not surely failed. The correlation between the campus and city is connected to physical componence of the campus within the city. However, the more critical element that university impact on the city is about the design of a campus. The point is, the more university campus integrated with the hosted city as a physical location has a stronger relationship in both spatial and socio-economic aspects.

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