This document describes a spatial decision support system called Location Intelligence that was developed to guide business investors in selecting suitable locations. It analyzes a wide range of location variables to characterize different business environments and neighborhoods. Through a prototype for London, the system matches investor needs to location profiles, aids decision making, and aggregates data on physical, social, human, knowledge, and productive capital factors. Location profiles developed from statistical analysis describe urban professionals, blue collar industry, finance, third sector, warehousing, retail, creative, tourism, and academic areas to support complex spatial business decisions.

1. Location Intelligence: a spatial decision support system for business site selectionDeveloped by:Dr Patrick WeberUniversity College Londonemail: p.weber@ucl.ac.ukTel: +44 (0) 7854840450

2. 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

3. 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.

4. Data BaseGeo-business classification

5. Relevant business locations geography:Town Centre Boundaries(Thurstainet al. 2001) Source: Thurstain-Goodwin & Unwin 2000 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)

6. Business Location Decision Making Variables 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

7. 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 describesdifferent 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

8. Geo-business Environments

9. Urban professionals 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. Most representative Cheapside Leadenhall Liverpool Street and Bishopsgate Holborn Canary Wharf Least representative Brent Cross Hendon Central Bexleyheath Chingford Mount Hornchurch

10. 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 Most representative Dagenham Bow Kenton North Tottenham Lower Edmonton Least representative Norbury Eastcote Pinner Brent Street Hampton Wick

11. 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 Most representative Leadenhall Cheapside Liverpool Street and Bishopsgate Croydon Retail Core Canary Wharf Least representative England's Lane Highgate Ruislip Manor Munster Road,Fulham St Margarets

12. Third Sector Centres Keywords: Third sector and caring professionals, deprived neighbourhoods, low value/quality retail and office premises Most representative Norbury North Kensington Brixton Kensal Town Maida Hill Least representative Upper Brompton Road Heathrow South Kensington Yiewsley Knightsbridge

13. Big Sheds and Trucks Keywords: Warehousing and Distribution economy, lower skilled workers, predominantly warehouses and factory space, almost no retail or financial services. Most representative Heathrow Hayes Town Erith Chiswick Brentford Least representative Kenton Dagenham Barnes PettsWood England's Lane

14. High (End) Streets Keywords: High value retail related activities and estate agents, local tourist attractions, professional workforce, relatively high value offices Most representative Upper Brompton Road South Kensington Stamford Hill Knightsbridge Kings Road,Chelsea Least representative Mitcham Eastcote South Harrow North Cheam Penge

15. Creative & Green Minds Keywords: Predominantly creative industry, ICT and environmental industry, large employers, few manual labour workforce Most representative Battersea Riverside Hammersmith Camden High Street LatchmereRoad, Battersea Kentish Town Least representative Heathrow Camberwell Seven Kings Upper Tooting Leadenhall

16. Sights of London Keywords: Focused around tourism and retail, along with high quality office space for professional and financial services. Most representative Bayswater Cheapside Leadenhall Liverpool Street and Bishopsgate Knightsbridge Least representative Yiewsley Tolworth Tooting Upper Tooting Richmond Bridge

17. Ivory Towers Keywords: Concentration of Life Sciences and Higher Education Institutions, accompanied by highly qualified and professionals Most representative Mill Hill Sudbury Hill Teddington Haverstock Hill Hampstead Least representative Mitcham Highgate Road Yiewsley Upper Brompton Road Wallington

18. Location profiles:

19. Prototype Implementation

20. Investor Decision Making Process:

21. 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

22. For more information and a demonstration of the system, please contact: Dr Patrick WeberUniversity College Londonemail: p.weber@ucl.ac.ukTel: +44 (0) 7854840450