Content
Liverpool is among the largest cities in the UK, located at the centre of the broader Merseyside economic region in the North West of England. The city has a population of about 1.3 million in its larger Urban Zone and 2,241,000 people in the Metropolitan area. The city’s employment sector is mostly covered by the three sectors, which are education, health and public administration. Commuter flows is very crucial for the transport planning of the city (Li et al.,2018). This is because it helps to determine the zone which gets high traffic density based on the number of people moving to it, hence upgrade the transport facilities leading to the region. It also helps to regulate or control the number of traffic entering into the city, through the use of traffic signals and traffic police, thereby avoiding traffic problems such as traffic jams. The various patterns of commuting in Liverpool include commuting through train, buses, taxis, motorcycle, private car or as a passenger in a car or van, using a bicycle or walking on foot.
Key issues
One of the major economic problems faced by Liverpool city is the shortage of office spaces. This has resulted from the lowered prices of office spaces in the city, hence discouraging developers from putting up offices since they perceive the investment as unprofitable. This has eventually damaged the city’s ability to attract more investments and jobs to the city( Wenger,2018). The city has also experienced an industrial decline which has deprived the city. Socially, the city has experienced great inequality between the poor and wealthy classes due to regeneration of areas. In the transport sector, the various regions of the town have a poor bus network of transporting people to various areas in the city. Such pronounced inequality is exhibited in areas such as housing and quality of education.
Children dropping out of school is equally another social challenge in the city. This has eventually led to reduced wages and increased unemployment in the city, for example Anfield has 9% of adults who are unemployed. Waste disposal has also been a challenge in the city due to its growing population( Wenger,2018). The emission of greenhouse gases like CO2 and traffic jams in the city centre is another problem experienced in the city centre. Lastly, smoking and drinking have also caused earlier deaths of some members of the population while at the same time, people leaving the city falls victims of vandalism, crime and graffiti. The city has a great opportunity in the area of putting up offices and housing facilities since it has a serious problem of lacking Grade A office spaces for business activities. Clustering helps to make sense and extract value from large data sets, either structured or unstructured. Clustering helps to take a sweeping glance of data en masse, then form a logical structure based on what can be derived from it before getting deeper into it.
Statistical Analysis
The data is acquired from the spatial variation in commuting flows and the variation it has with other variables in the entire Liverpool city. The data utilizes the 2011 census of Liverpool city population. The assessment relationship has been made between the mean distances that are travelled by the commuters and the other socioeconomic variables of the city (Ferraresi,2019). The Bayesian inference, estimation technology and common special factor methods are the methods employed on developing a summarized statistic on the major commuting aspects. The above methodology is oriented to create summarized measures of the various commuting attractiveness of the various places to form destinations and the varying dependence of various places to form origins beyond employment (Patuelli.,2007). Intra area flows have also expressed varying degrees of commuting self -containment in various areas. Certain studies of spatial interaction such as recreational flows and migrant flows have tried to develop summary scores, more so for the varying attractivity of areas. The collection of the effects of commuting in a city are considered as random, explicitly enhancing an allowance for spatial patterning in their derivation. Commuting flows from a certain origin get enhanced according to the level of permissiveness of the neighbouring origin locations while flows associated with particular destinations get enhanced according to the level of attractiveness of its nearby destination locations. These types of proximity effects are an implication of potential spatial clustering in the extra-dependence and attractiveness of commuting.
SI modelling framework helps in exploring commuting between various places within the city. Conventional SI modelling entails a regression in which case, the moving workers between places is considered the dependent variable and the predictors is usually the origin-destination distance and the number of jobs(people) in the origin and destination. Generally, calibration of the model using log-linear regression having few errors is widely utilized as an approach to SI modelling a log-linear regression, the conditional relationship existing between two or more discrete, categorical variables gets analyzed by determining the natural algorithms of the cell frequencies within the contingency table. Loglinear modelling involves fitting of the models to the observed frequencies within the cross-tabulation of the categoric variables (Li et ai.,2018). A Poisson regression, on the other hand, assumes that the response variable Y has got a Poisson distribution, and also assumes that the logarithm of its expected value is capable of being modelled through a linear combination of unknowns. Moreover, it involves a condition where an overdispersed version of Poisson density has been assumed as the fundamental model, and the potential commuting effects gets assessed after controlling both inter-zone distances and mass effects.
Key recommendations
First, the committee should prioritise coming up with up strict regulations on the number of traffic entering the city centre of Liverpool city. This is in a bid to control the release of harmful CO2 gas, which is a heavy pollutant of the city’s environment. Moreover, the regulation of the number of traffic entering the city will also control the problem of traffic jam within the city. Secondly, the government should initiate a program of building more housing facilities and offices in the city centre of Liverpool. This is to aid in expanding businesses in the cities CBD.
Caveats
The Bayesian estimation provides a complete posterior density of the commuting data, enabling formality of inferences on the cities commuting relativities for example, which region has the highest commuting attractivity and which area is highly extra dependant in commuting terms than the other. It also involves specification of prior densities on all unknown conditions after which the densities are updated through their likelihood of the observations.
References
Wenger, G. C. (2018). A comparison of urban with rural support networks: Liverpool and North Wales. Ageing & Society, 15(1), 59-81.
Patuelli, R., Reggiani, A., Gorman, S. P., Nijkamp, P., & Bade, F. J. (2007). Network analysis of commuting flows A comparative static approach to German data. Networks and Spatial Economics, 7(4), 315-331.
Li, D., Zhou, X., & Wang, M. (2018). Analyzing and visualizing the spatial interactions between tourists and locals: A Flickr study in ten US cities. Cities, 74, 249-258.
Ferraresi, M. (2019). Political cycles, spatial interactions and yardstick competition: evidence from Italian cities. Journal of Economic Geography.