location intelligence: an innovative approach to business location decision making

Location Intelligence: a spatial decision support system for business site selection

Developed by:Dr Patrick WeberUniversity College Londonemail: p.weber@ucl.ac.ukTel: +44 (0) 7854840450

Copyright Dr. Patrick Weber: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

Data BaseGeo-business classification

Copyright Dr. Patrick Weber: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

• 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: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.ukUrban 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: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

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

Copyright Dr. Patrick Weber: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

Location profiles:

Copyright Dr. Patrick Weber: p.weber@ucl.ac.uk

Prototype Implementation

Copyright Dr. Patrick Weber: p.weber@ucl.ac.uk

Investor Decision Making Process:

Copyright Dr. Patrick Weber: p.weber@ucl.ac.uk

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: p.weber@ucl.ac.uk

For more information and a demonstration of the system, please contact:

Dr Patrick WeberUniversity College Londonemail: p.weber@ucl.ac.ukTel: +44 (0) 7854840450