How Does The Land Use Change With Distance East Of Liverpool Street Station?
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- Sep 8
- 13 min read
Updated: Sep 9
Author: Eason Turner, King's College London Maths School, London
Hypothesis
The land use from Liverpool Street Station will change from Shops -> Offices/Industrial sites -> Residential with increasing distance east from Liverpool Street Station one.
The footfall will decrease further from Liverpool Street Station.
The quality of street/building maintenance will decrease as will the building condition which, in turn, means the Environmental quality survey score will also decrease.
Expectations
One would expect that, in the immediate proximity of Liverpool Street Station, there would be many shops and food stalls to service passengers arriving/departing. Then, as one moves away from the station, the environment transitions from retail spaces to offices and businesses. At a certain distance from Liverpool Street Station, one would expect more residential areas. The reason being that land near to Liverpool Street Station is of high value and therefore revenue generating businesses are the most likely tenants. As the area becomes more residential, then footfall will decrease. Land costs decreases with increasing distance from the station making it more suitable for homes.
Theory
This investigation is formed upon the basis of the bid rent model which dictates that the Central Business District (CBD) will be situated on the roads with the highest footfall and will have the highest rent prices with the CBD then transitioning into offices and industry which, while still having higher rent values than residential buildings, will generally be cheaper than within the CBD.
CBD usually forms around areas with advanced travel connections as well as in places of high footfall. This is due to the increased number of potential clients which means many shops are willing to pay more for the buildings in the area as they have a high financial incentive due to the likelihood of higher revenues than in more remote locations. On the other hand, potential home owners are not as likely to pay as much as high footfall is less of an influence. This results in a higher density of shops purchasing or renting properties over residents in busy areas such as Liverpool Street Station.
As one gets further away from these areas, the footfall decreases naturally thus the average price will decrease as shops have less motivation to locate themselves there. This then leads to more residents as it becomes more affordable for house owners. Similarly to this, offices will pay more for well-connected locations with fast ease of transport as it is easier for employees to commute, resulting in a higher available workforce. For example, Liverpool Street Station is on five tube lines and also has many national rail connections making it extremely easy to travel to in a multitude of locations.
While companies prefer locations next to stations or travel networks, they don’t need the high footfall since their business is less reliant on passing traffic and visibility to the public. This results in many establishments choosing to locate close to a tube station but not directly on the main road. Consequently, in the middle ring, as shown in figure 1 many businesses choose to base themselves on streets with low footfall around the station which, whilst they are unsuitable for shops, are more suited to companies. This reaches a limit at a certain point from the station as companies are less willing to pay higher rents due to the increased travel times for workers; thus it gradually transitions into residential areas as the cost of space decreases further from the CBD and becomes financially accessible for individuals.
Location: We decided to collect data at Spitalfields, a small area in the heart of London. We decided upon this location as it is relatively nearby being a 25 minute direct tube journey from Westminster which allowed us to maximise our time to collect data. The vast majority of roads required also had traffic lights and pedestrian crossings making it a moderately safe route.
Background
Spitalfields is a small market located in Tower Hamlets in East London. It has a rich and vibrant history dating back to the 1600s. The market was first recognised by King Charles in 1666 and was located directly outside of the main city’s gates, Bishopsgate. Spitalfields has a large background of immigrants beginning with French Huguenots who brought with them the silk trade. However, when trade opened with India and silk became cheaper to buy, many Huguenots moved to America. Subsequently, they were then replaced by Irish immigrants who moved during the Irish potato famine looking for employment in the 1840s. For the next few decades London continued to grow and this led to many issues such as overcrowding with associated issues such as crime and poverty which had a huge impact on the market which started to fall into decline.
Following this, many Jewish people moved to Spitalfields offering services in tailoring and fashion. Post-war, many Bangladeshis moved to the area. In 1991 the market moved out of London due to congestion in the surrounding area to the suburbs of Leyton with the creation of New Spitalfields Market. The old market space was redeveloped in 2005. This period marked a large change for Spitalfields as it turned from a fresh produce market to space for designers and artists to sell clothes, jewellery and art. Over the most recent 20 years, there has been a huge wave of gentrification near Spitalfields, with many tech companies and businesses moving to the area. This has resulted in many shops catering to the new customer base with coffee shops and restaurants seeking to provide for the many workers in the area.
Risk Assessment
With any project such as this, there are many risks that need to be evaluated and mitigated for optimal resolution of problems that may occur. For this we decided to use a probability and impact matrix (PIM). We used this to categorise the problems into 3 types: High, medium, and low threat depending on the possible impact that it would have and the chance of its occurrence. This was then applied to many aspects of the trip such as scheduling and data.
Threat Levels:
Low: These risks are ones which have limited chance of occurring and having an impact on the project.
Medium: These risks have a chance of potentially hazardous consequences and so required a mitigation or avoidance plan
High: These are the highest level of risks that can have disastrous effects and so these have the most thorough strategies.
Data Collection
There were three main techniques used to collect the data – each of which helped to provide key data for determining the changes that occur as you move East from Liverpool Street Station. We collected this data at 11 different points with each being an increasing distance away from the station.
Method 1: Footfall
To measure the footfall in an area we counted the number of pedestrians going in a single direction on the pavement in 30 seconds which we then repeated on the other side of the road. The number of up our group into two teams of two people who each did one side of the road simultaneously. Then we added up the two scores to get the total number of pedestrians at that time.
Justification: We decided upon this method as it is efficient, accurate and fast. As each sub-group was only measuring one direction, it was extremely accurate as both members were counting to ensure that no one was missed or people being double counted. Measuring for only one minute also meant that we could quickly and easily cover the required areas and that we were able to complete the survey within the timeframe.
Method 2: Environmental Quality Survey
For this, we ranked each area in four different categories: Quality of decoration, building maintenance, street maintenance and litter. Each one was given a score of 1-5 ranging from poor (1) to very good (5). All members in the team filled out the survey and an average score for each area was determined.
Justification: This visual inspection gave us an idea of the makeup of each area and allowed us to determine the level of maintenance and cleanliness of the area. We correlated this with the level of investment in the area. It also provides a basic overview of the standard of the properties.
Method 3: Land Use
For this, a group of three mapped the designation of each building’s first floor along the route in between the sites. Each building was put into a different category: Residential, service, commercial, entertainment, open space or public building. This data was then transferred onto a map which was then colour coded to aid identification of any patterns (see land use map).
Justification: The land use is a key piece of data, helping to establish the uses of the blocks in the surrounding area. An integral part of the Bid Rent model which we are trying to establish the reliability of in this research project. Additionally, as we were only doing the ground floor of the building we can deduce with reasonable certainty the allocation of the building.
Justification of sampling strategy
For each of the methods we used a systematic method. This is because it gave a fair and even representation of the areas whilst also being extremely time efficient. Using the data we were able to make several ArcGIS maps presenting our findings which made the data easy to view and draw conclusions from.
Results
Footfall
Environmental Quality Survey
Land Use
ArcGIS map representing EQS and Footfall
Interpretation
Footfall
From the graph we can see a significant decrease in population from site A to site B and again from B to C. From A to B there was a more than 50% decrease in footfall, then a another 70% decrease from B – C. This is mainly due to Site A being situated on the main road, with many roads running through it resulting in high usage. While B is slightly off the main road, there are a multitude of shops and other services that result in a high number of people.
After this, as shown in the land use map, it gradually transitions into more residential and industrial buildings rather than services which means a decrease in footfall due to fewer reasons for travelling through that area as is illustrated by the graph. Sites C, D and E had an average of 8 while H, I and J have an average 4 showing the decrease in use of the roads. Although, site K had a count of 11, this is likely due to the data of site K being recorded later in the day – closer to lunchtime. This would result in a larger number of people being outside thus a higher total.
Environmental Quality Survey
From the data shown, contrary to the footfall, site A and B have a lower score than C with the scores 16, 17 and 19 respectively. This is partly owing to the lower building maintenance/decoration scores. Site C was built much more recently thus, naturally, is in much better condition than site A and B. Moreover, Site A has several grade 1 and 2 buildings that were built several decades ago and although they have historical significance to the area they may not be considered visually aesthetic as they were built post WWI and WWII and consequently were built for efficiency and with limited resources. Additionally, due to site A and B’s increased footfall, a lower litter score is expected as there are more people passing through the area which has a direct correlation with litter.
Sites D-F had some of the highest scores of the places we surveyed. This is due to many businesses investing in new offices resulting in high scores for quality of decoration with an average of 4.7, however had a lower street maintenance cost because of the road’s lower use, less money was spent on maintenance.
From sites G – K each score was in the range of 9-14 with almost all getting a score of 3 on quality of decoration. This is due to many of the buildings being residential, many council housing, and so were built with cost effectiveness in mind rather than visual attractiveness.
Land Use
Close to Liverpool Street Station from site A - E, there were many shops and entertainment businesses. This is due to the bid rent demographic which states that shops and entertainment businesses will have more of an incentive to pay more money for buildings with high passing footfall as this can greatly impact their sales, whereas homeowners have less of an incentive as there is usually no benefit (and usually some disadvantages) to being on a busy road.
There was then a sharp jump at site F where almost all of the buildings were residential. This is due to the road transitioning into a smaller passageway which was unattractive for businesses as there was very low footfall (we measured 120 people per hour compared to 5400 at site A). This meant that very few shops were willing to pay more than residents for the location.
Subsequently, at site G and H there were more commercial and entertainment buildings mainly due to the Spitalfield’s Market located nearby. Many travel to the market each day, making the nearby buildings highly sought after since the market attracts many customers each day and offers a higher chances of potential business.
Then, further along the road – specifically sites I – K – almost 85% of all buildings were residential from around 10% at site E. Additionally, this is where we found the highest density of open space – this is to cater for the nearby residents for leisure activities as well as being due to the lower cost of land nearby compared to earlier sites. This land is mainly residential as there is a limited number of people travelling through the area as there are few shops, offices or entertainment centres nearby.
Limitations of results
Footfall
One of the main limitations of this method is that, while the data is mostly accurate (while miscounting is possible, it is unlikely due to two people counting) for the time when the data was collected, it doesn’t fully show the footfall for the area. This is due to variations in footfall at different times, for example, lunchtime would be significantly busier than normal due to many going out for lunch as we started collecting data at around 10am and finished at around midday thus skewing the data.
Another key limitation is that footfall was only measured for 30 seconds. This meant that significant variations in results could be found. To illustrate this, at several points the pedestrians counted numbered from 1-6. This means that a singular person, or a group walking together, could significantly change the results found making the results extremely vulnerable to anomalies.
Improvements: With more time this could be easily improved by repeating the count, each for longer periods of time, at different times of day and different days of the week. This could even be taken further and at different times of year such as in the summer when there would be more tourists compared to winter (where bad weather could also impact the findings). This would give a much more detailed overview of the number of people in the areas over the course of the year.
Environmental Quality Survey
An obvious and major flaw in survey is the subjective nature of the assessments. As a result of the time constraints, as well as safeguarding issues, we were not able to ask the public for their opinion on this matter. However for this to be an unbiased survey we would have needed to ask a large number of people for their views. Some of the main shortcomings of qualitative methods are that it is subjective and that opinions and standards vary from people to people. It could be argued that quantitative methods are a better way of determining a score – such as bins/100metres. Our method meant we had very limited viewpoints with only the four team members contributing to the score.
We also found that sometimes the categories weren’t obviously a score and that only having five potential scores was a big limitation which led to a debate as to which score we should give it (such as with the litter section there was often little difference between a three and a four score) which decreased the accuracy of the experiment.
Improvements: With more time and resources, a much larger group of people from the general public could’ve been asked – helping to get a much more unbiased result and more informed results due to the range of opinions from many people.
Land Use
We decided that it would be best to only note down the land use of the ground floor of the building due to the difficulty in discerning the use of the floors from the limited external view. This will have skewed our results as generally shops are located on the ground floor of buildings for easy pedestrian access, with the floors above generally used as offices or residential. This meant that we had a higher percentage of shops to other buildings than we would have had if we included the upper floors in the survey.
It could also be said that the area occupied by certain ventures would be an important value to measure. Many residential buildings are usually much smaller compared to commercial industries and companies generally have larger offices to accommodate a higher number of personnel. By ignoring the area of the building in our analysis we have possibly overstated the residential buildings in comparison to commercial buildings.
Improvements: A potential improvement would be to access secondary data detailing the land uses from a much wider area across all floors. This would help to provide data for the buildings that could not be accessed as well as the upper floors of buildings. Across all sections an improvement that could be made is taking results from different directions from Liverpool Street Station, helping to provide evidence for whether our original hypothesis is true in all directions. As Liverpool Street station is located to the north of the City of London and to the East of London generally, this could produce some interesting results.
Conclusion
Footfall: From the graph we can clearly see a big decrease in the number of people from site A to site B however there is no clear trend for the rest of the points. However, it is still possible conclude that there is a big drop in the number of people from directly outside Liverpool Street to an offcut of the street. To confirm findings, we would have to return and undertake the improvements outlined earlier.
Environmental Quality Survey: We can deduce from our data that there is a negative change in score as we progressed further away from Liverpool Street Station. Although, our data is unreliable as our survey sample size of four people was too small to reach a confident conclusion as to whether our hypothesis was correct.
Land Use: In our diagram we can see a trend of commercial and entertainment buildings close to Liverpool Street Station with very few open spaces and residential blocks; this then transitions into a phase of residential buildings followed by more commercial and entertainment. However, for the majority of sites I to K, the buildings were for residential use. Thus we can conclude that our original hypothesis has merit and has a correlation, although, similarly with the other hypothesis the accuracy can be improved.
Overall, we can meet the assumption that the Bid Rent does have weight however can be studied further for increased certainty.
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