Big Data Analytics & Placemaking

 

At Smart Urban Management we are pioneers in the use of big data analytics in place-making. We work with local authorities in regeneration, economic development, master-planning, and transport planning.

 

The detailed insights we can derive from our analytics can be put to use in the following areas:

 

Town Centre Health Checks ­– detailed information of opening/closing hours, employment data, spend and customer sentiments can all contribute to a more thorough health check.

 

Evening & Night Time Economy – understanding the ENTE from a user perspective can often unearth previously over looked opportunities/problems in the development of ENTEs. Understanding user sentiment (types of establishments preferred by visitors), activity patterns, competitor analysis, and detailed movement patterns can all help create a much deeper understanding of the ETNE.

 

Inward Investment Plans – Analysis of competing areas can help understand the type of businesses you need to attract to your area. Understanding the behaviour patterns of visitors to competing areas can also lay out the type of environment you need to create to attract those or similar visitors. Reviewing what is currently working and what needs to be replaced by analysing movement patterns and spend data can help formulate a robust inward investment plan that will attract the ideal type of business for your area.

 

A snapshot analysis of visitor activity patterns around Stamford Bridge on match and non-match days. The blue colour shows non-match day activity and pink colour match day. It is clear to see a considerable drop in activity on match days.

Points of Interest & Major Entertainment and Sporting Venues – Our intensive data analytics can help you understand how users interact with POIs and the effects they have on the local public infrastructure and economy. Are your POIs being used to the maximum advantage of your area? How do major entertainment and sporting venues effect the local economy? Are the types of questions we can not only answer but provide ready to implement action plans to mitigate negative impacts and to capitalise on opportunities.

 

Transport Planning – Our analysis of detailed movement patterns including: origin and destination, modes of transport, traffic hotspots, underused routes, dwell times, and parking can help local authorities make comprehensive transport plans for their areas. Our analytics team can place their analytics into advanced traffic modelling software’s to help plan major highways and road plans.

 

 

 

 

Master-planning and Housing – We can assist in the development of major master-plans and housing schemes. Our work can help authorities and developers understand both existing and new incoming communities, impacts on public infrastructure and curation of commercial units.

 

The data sets and most commonly used metrics we use are detailed below, these metrics can be used in a number of other key areas including; community development, health & well being, digital high street projects and others. Please get in touch to discuss your requirements and how our work can help you.

Data Analytics & Metrics

Big data analytics is driven by the application of various metrics to support the decision making progress of stakeholders. These metrics help urban practitioners to make informed, accurate and sustainable decisions. Smart Urban Management (SUM) data sets include:

  •  
  • Cellular 
  • Social Media
  • Open source - public
  • Travel data
  • Card transactions

 

Depending on the focus and outputs of the projects, SUM is able to collect and process extensive information and granular details on the following metrics:

Movement

Mobility patterns – directional footfall numbers, desired paths etc.

  • Origin and destination area of visitors
  • Transport modes (public, private, cycling, walking etc.)
  • Distance and time travelled
  • Dwell time
  • Patterns around ‘points of interest’
  • Congestion and bottlenecks
  • Hotspots of activity
  • Under-used areas and times
  • Key transit places
  • Catchment areas – (origin of visitors)

Behaviour

Socio-demographics (ethnic origin, social status etc.)

  • Discussion topics – around events, incidents, points of interest etc.
  • Sentiments analysis
  • Sematic links – identify competing places
  • Security perceptions

Economics

Transaction Volume

  • Commercial diversity (sector break up etc.)
  • Business hours
  • Commercial real estate rents
  • Commercial capacity – bench-marking sentiments against transaction volumes
  • Spend satisfaction

These metrics do not include those that can be measured using existing data sets urban practitioners may already possess such as those related to licensing, planning, enforcement, crime etc. New data can be analysed together with existing data to identify bespoke places and the relationship patterns occurring within the urban realm.

 

SUM is pioneering the application of big data analytics to place making. For further information please get in touch using the get in touch tab at the top or the email address below.