location intelligence: an innovative approach to business location decision making
DESCRIPTION
As one of the leading ‘world cities’ London is home to a highly internationalised workforce and is particularly reliant on these sources of foreign direct investment (FDI). In the face of increasing global competition and a very difficult economic climate, the capital must compete effectively to encourage and support such investors. Through a collaborative study with London’s official foreign direct investment agency, Think London, the need for a coherent framework for data, methodologies and tools to inform business location decision making became apparent. This presentation will discuss the development of a rich environment to iteratively explore, compare and rank London’s business neighbourhoods alongside ancillary data. This is achieved through the development, integration and evaluation of data and its manipulation to form a model for locational based decision support. Firstly, we discuss the development of a geo-business classification for London which draws upon methods and practices common to many geospatial neighbourhood classifications that are used for profiling consumers. In this instance a geo-business classification is developed by encapsulating relevant location variables using Principal Component Analysis into a set of composite area characteristics. Secondly, we discuss the implementation an appropriate Multi-Criteria Decision Making methodology, in this case Analytical Hierarchy Process (AHP), enabling the aggregation of the geo-business classification and decision makers preferences into discrete decision alternatives (Carver 1991; Jankowski 1995). Lastly, we present the preliminary results of the integration of both data and model through the development and evaluation of a web-based prototype and evaluate its usefulness through scenario testing.TRANSCRIPT
Location Intelligence: a spatial decision support system for business site selection
Developed by:Dr Patrick WeberUniversity College Londonemail: [email protected]: +44 (0) 7854840450
Copyright Dr. Patrick Weber: [email protected]
Location Intelligence - Overview of benefits
• Guide and inform investors on suitable locations:– Based on investors individual needs and demands– Using a consistent, quantifiable methodology– Combining a wide set of relevant location variables
• Quantify and qualify region’s diverse business locations:– Formalise and highlight different locations offer to investors– Guide investment to alternative areas (e.g. outside Central Business
District)
• Record and analyse investors decision making processes:– To gain a better understanding of location factors influencing decision
making processes. – Matching Demand (investor needs) and Supply (location offer)– Develop location intelligence that can be fed back to stakeholders
Copyright Dr. Patrick Weber: [email protected]
System capabilities and benefits demonstrated through prototype implementation for London, UK:
– Understand London’s business environments through the characterisation of London’s business neighbourhoods (at an appropriate spatial scale).
– Aid business location decision making, qualifying and quantifying location profiles according to investor needs.
– Develop an integrated toolset supporting these complex spatial decision making processes.
Copyright Dr. Patrick Weber: [email protected]
Relevant business locations geography:Town Centre Boundaries(Thurstain et al. 2001)
• Consistent boundaries across England and Wales.
• Statistics covering employment and floorspace.– Define consistent & relevant set
of boundaries for London “Villages”
– Economic Activity measured (80% of total London employment in & around TC)
Source: Thurstain-Goodwin & Unwin 2000
Copyright Dr. Patrick Weber: [email protected]
• Physical Capital– Infrastructures and
facilities – Environmental Services &
Infrastructure– Commercial & Residential
Property
• Social Capital– Public Services – Healthcare
• Human Capital– Labour force data – Socio-demographic data
• Knowledge Capital– Research infrastructure– Labour force data
• Productive Capital– Company Data– Business Intelligence
Business Location Decision Making Variables
Copyright Dr. Patrick Weber: [email protected]
Statistical Analysis & Aggregation of Location Variables
• Reduces complexity of location variables– Components characterise as completely as statistically achievable, both
the common and unique variance of the original variables.
• Analysis describes different aspects of Town Centres. – aggregating positively and negatively correlated variables.– Component scores quantify likeness of individual town centres
• Develop from components rich profiles describing different business environments
Geo-business Environments
Copyright Dr. Patrick Weber: [email protected] professionals
Most representative1. Cheapside
2. Leadenhall
3. Liverpool Street and Bishopsgate
4. Holborn
5. Canary Wharf
Least representative6. Brent Cross
7. Hendon Central
8. Bexleyheath
9. Chingford Mount
10. Hornchurch
Keywords:Professional and financial service economy, mix of large & small employers, skilled managerial and professional employees, land use predominantly high quality offices, limited retail space.
Copyright Dr. Patrick Weber: [email protected]
Most representative1. Dagenham 2. Bow 3. Kenton 4. North Tottenham 5. Lower Edmonton
Least representative6. Norbury 7. Eastcote 8. Pinner 9. Brent Street 10. Hampton Wick
Blue Collar Industry
Keywords:Manufacturing, food and drink as well as distribution economy, mix of large and small employers, routine and technical employees, land use predominantly warehousing, limited office space
Copyright Dr. Patrick Weber: [email protected]
Most representative1. Leadenhall
2. Cheapside
3. Liverpool Street and Bishopsgate
4. Croydon Retail Core
5. Canary Wharf
Least representative6. England's Lane
7. Highgate
8. Ruislip Manor
9. Munster Road,Fulham
10. St Margarets
Blue Chip Finance
Keywords:Financial services economy, large employers, skilled managerial and professional employees, predominantly offices, no tourism attractions, few self employed workers and small employers
Copyright Dr. Patrick Weber: [email protected]
Most representative1. Norbury
2. North Kensington
3. Brixton
4. Kensal Town
5. Maida Hill
Least representative6. Upper Brompton
Road
7. Heathrow
8. South Kensington
9. Yiewsley
10. Knightsbridge
Third Sector Centres
Keywords:Third sector and caring professionals, deprived neighbourhoods, low value/quality retail and office premises
Copyright Dr. Patrick Weber: [email protected]
Most representative1. Heathrow
2. Hayes Town
3. Erith
4. Chiswick
5. Brentford
Least representative6. Kenton
7. Dagenham
8. Barnes
9. Petts Wood
10. England's Lane
Big Sheds and Trucks
Keywords:Warehousing and Distribution economy, lower skilled workers, predominantly warehouses and factory space, almost no retail or financial services.
Copyright Dr. Patrick Weber: [email protected]
Most representative1. Upper Brompton Road
2. South Kensington
3. Stamford Hill
4. Knightsbridge
5. Kings Road,Chelsea
Least representative6. Mitcham
7. Eastcote
8. South Harrow
9. North Cheam
10. Penge
High (End) Streets
Keywords:High value retail related activities and estate agents, local tourist attractions, professional workforce, relatively high value offices
Copyright Dr. Patrick Weber: [email protected]
Most representative1. Battersea Riverside
2. Hammersmith
3. Camden High Street
4. Latchmere Road, Battersea
5. Kentish Town
Least representative6. Heathrow
7. Camberwell
8. Seven Kings
9. Upper Tooting
10. Leadenhall
Creative & Green Minds
Keywords:Predominantly creative industry, ICT and environmental industry, large employers, few manual labour workforce
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Most representative1. Bayswater
2. Cheapside
3. Leadenhall
4. Liverpool Street and Bishopsgate
5. Knightsbridge
Least representative6. Yiewsley
7. Tolworth
8. Tooting
9. Upper Tooting
10. Richmond Bridge
Sights of LondonKeywords:Focused around tourism and retail, along with high quality office space for professional and financial services.
Copyright Dr. Patrick Weber: [email protected]
Most representative1. Mill Hill
2. Sudbury Hill
3. Teddington
4. Haverstock Hill
5. Hampstead
Least representative6. Mitcham
7. Highgate Road
8. Yiewsley
9. Upper Brompton Road
10. Wallington
Ivory TowersKeywords:Concentration of Life Sciences and Higher Education Institutions, accompanied by highly qualified and professionals
Copyright Dr. Patrick Weber: [email protected]
Web based service:
• Combining data base (geo-business classification) with Multi-Criteria Decision Making Framework
• Visualisation of results using “Google Maps” interface:– Lightweight Web Client (database and computation on web server)– Interactive visualisation of results through maps, graphs and
statistics– Potential for integration of external data (statistical, properties ...)
• Evaluates geo-business classification + accessibility:– Potential to integrate other variables, develop custom decision
trees according to client needs and data availability
Copyright Dr. Patrick Weber: [email protected]
For more information and a demonstration of the system, please contact:
Dr Patrick WeberUniversity College Londonemail: [email protected]: +44 (0) 7854840450