BA 302 - Managing Space - Site Selection / Facility Location

BA 302 - Managing Space - Site Selection / Facility Location - GIS

Problem: Does 'space' or 'location' matter in business operations? Is geography really "history"?

Learning outcomes:

Recognize spatial/locational aspects of operational processes.

Recognize how to use spatial information to make spatial decisions.

Recognize the role of Geographic Information Systems (GIS):

Know the basic difference between vector and raster GIS.

Realize that 'flat' maps are projections that imply distortion.

Realize that different projections imply different distortions.

Realize that GIS ≠ mapping.

Regression as a way to find a pattern in data (not at all limited to spatial data!!!!).

Recognize additive utility model and linear regression model as having the same mathematical form.

Single or infrequent vs. multiple or frequent site selection:

Single/infrequent:

Car manufacturer wants one or two production plants in North America.

Fujitsu needs a new call center.

Where does the new nuclear power plant go?

Where will I put my Indian-/US tax filing business?

Where do I establish my one-person furniture-making business? My restaurant? My bed-and-breakfast?

If I'm Google and I establish a new data center in The Dalles, OR, can I attract sufficient highly-trained personnel?

If I'm Google, why would I want to be in The Dalles anyway?

Can I spatially separate my front office from my back office (decoupling)(p. 127, 228)? Why would I want to do that?

Multiple/frequent/recurring:

Where do the next 10 Starbucks' go?

Where to locate mail (drop off) boxes?

How do I lay out bus lines through the city and where to put in stops?

If I shuttle people to/from the airport and back, where do I have (fixed) stops?

If I shuttle people to/from the airport who do I drop of first, second, etc.?

Which route to follow when delivering pizza? --> traveling salesman problem.

Which taxi gets dispatched to which address?

Overcoming friction of distance implies:

using time ==> cost.

transportation ==> cost.

uncertainty (space is not always identically configured; e.g., empty vs. crowded roads).

Some thoughts on site/location selection (Keep in mind that geographers have filled libraries with this topic):

Flexibility: space to expand? ease of move?

Proximity of demand:

Hotels near airports, downtown, end of town on the highway.

Gas stations at major highway exits.

Flower shop in hospital and near the cemetery.

Jewelers, bookshops and cafés downtown.

Jewelers, bookshops in shopping malls.

Etc.

Proximity of inputs:

Coors Brewing CO. @ Clear Creek in Golden, CO.

Google The Dalles close to inexpensive, uninterrupted electrical power.

Google The Dalles not too far away from Portland, OR.

ALCOA aluminum making @ TVA.

Paper/pulp mills in forested areas or in harbors (transfer point of bulk transport).

Etc.

Competitive positioning:

Competitive clustering:

furniture/car row, Zürich's Bahnhof Straβ (high end stores)

Hotelling's ice cream vendors (Waikiki Beach example p. 138).

Saturation marketing: Bon Pain example, p. 129.

Decoupling: separating front- from back office:

Back office in far less expensive spaces/places.

Centralize back offices for economies of scale.

Information technologies makes all the world a back office.

e-distance on the Internet:

An unfound/unregistered site is infinitely far away.

Clumsy, multi-click 'roadways' put services and products 'far away.'

A 'centrally located' (easy to find, highly visible) Website can bring business to an otherwise remote business.

Site selection techniques:

Regression analysis (p. 132):

Collect two types of data:

Performance data on similar businesses; e.g., existing Starbucks stores, fast food restaurants, hotel franchises (La Quinta example (Table 10.3), bank branch offices, etc.

Spatial/locational information for each of these businesses; e.g.,
- Neighborhood income.
- Proximity to working population.
- Pedestrian traffic.
- Car traffic.
- Etc.

Model the performance of the businesses as a function of the locational characteristics:

Business performance = f(Per capita income, Proximity to shopping center, ..., Variable_n).

Business performance = α + β₁(Per capita income) + β₂(Proximity to shopping center) + ... + β_n(Variable_n).

Performance is the dependent variable.

Income, Proximity, etc. are the independent variables.

β₁, β₂, ..., β_n are called the regression coefficients.

Regression coefficients indicate how much the dependent variable will change if an independent variable changes one unit.

How to find β₁, β₂, ..., β_n? ==> least squares criterion; i.e., find values for β₁, β₂, ..., β_n such that the squared differences between the observed values and the predicted (model) ones are minimized.

Use the found βvalues and the spatial/locational information of a new site to predict the performance of the business at that site.

Problem: can you see some potential problems with this approach?

Model assumes that the dependent variable is a linear function of the independent variables (guess what will happen if we put in an exponential data set?

Model makes strong assumptions about the data and their distribution.

Every site is special; the model might not be valid for a specific site.

How to get the data with which to fit the model?

Have lots of other similar business establishments (Starbucks, Banks, Hotels, etc.).

Use trade organization.

Use specialized consulting company.

Collect your own using a Geographic Information System and available spatial data.

Explicit siting/localization methods (p.138-146)

Minimize x; e.g., mean travel distance of customers ==> Hotelling problem above.

Minimize x while maximizing y; e.g., percentage of high income households.

Use the appropriate distance metric; e.g., Euclidean vs. metropolitan (city-block) metric (p. 139).

Gravity methods (p. 143-144): customers (cities, towns, neighborhoods) and businesses 'attract' each other in proportion to their sizes and inverse proportionally to their distance. (Requires that this gravitational relationship exists!).

Geographic Information Systems (GIS):

GIS: software that links spatial to nonspatial information.

GIS: software for doing spatial calculations.

"A system for capturing, storing, checking, integrating, manipulating, analyzing and displaying data which are spatially referenced to the Earth." (UK Dept. of the Environment, 1987).

Problem: what do we mean by 'spatially referenced?' ==> things have a location in space: longitude, latitude and optionally elevation.

Absolute position.

Relative position:

Albany is downstream of Corvallis on the Willamette River.

Albany is north of Corvallis.

Madagascar is an island in the Indian ocean off of the southeast coast of Africa.

The problem of projections:

On the earth's surface things have longitude/latitude coordinates; Can get with techniques such as GPS.

However, expensive to collect if we must map many objects; e.g., homes and land, sewer lines, etc.

==> digitize locational information from maps.

Problem: earth is a sphere; map is flat ==> map is not accurate; has distortion:
Therefore, when combining several map layers, must always transform into a single, common coordinate system.

Vector or raster?
- Vector: All things spatial are either points, lines or polygons.
  - Points have no size, just position.
  - Lines are points connected; have no width.
  - Polygons are lines connected in sequence.
  - Infinite precision.
  - Great for building complex spatial structures such as route and sewer networks.
  - Low data volume requirements.
  - Very good for topological computations (relative position).
  - Complex computing.
- Raster: Space is 'quantized' in discrete, square chunks:
  - Everything has size; even points and lines.
  - Precision is limited to the size of the raster cell (pixel).
  - Need more detail? --> make cells smaller --> more data.
  - Easy computing.
  - Goes well with satellite imagery.
- Vector and raster can be converted into each other but:
- Some vendors:

Test questions:

In another textbook on service operations Metters et al. discuss site selection for 'delivered' services; e.g., ambulance, pizza, meals-on-wheels, FedEx/UPS/DHL, etc.

Problem: What are some of the spatial considerations that factor into this decision?

Problem: Would you favor a vector or raster GIS for this problem?

As the US Bureau of Land Management (BLM) you worry about non-native grass infestations across the Western US. You rely on satellite imagery to inventory populations of these species.

Problem: Would you favor a vector or raster GIS for this problem?

Can you think about possible applications of GIS in marketing? After all, doesn't one of the four p's of marketing stand for 'place?'

Why would moving companies be keenly interested in GIS technology?

Watch the (32 sec.) driving boxes video. See any connection with GIS technology?

Think of some uses for GIS in insurance, mortgaging and personal lending.