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ARP > Spatial Interaction Modelling > Flashcards

Spatial Interaction Modelling Flashcards

1
Q

What is Sij in the SIM?

A

The flows between i and j
i = origin/demand zones
j = destination/supply zones

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2
Q

What is Ai in the SIM?

A

A balancing factor that takes into account competition and ensures all demand is allocated to stores in the model

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3
Q

What is Oi in the SIM?

A

Demand - amount of expenditure in zone i

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4
Q

What is Wj in the SIM?

A

Store ‘attractiveness’ - normally size = attractiveness

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5
Q

What is exp-betaCij in the SIM?

A

Cij = cost of travelling between i and j - thought to be that greater distance means less attractive
Beta used for calibration

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6
Q

Why are SIMs more advantageous than buffers?

A
  1. Not confined to shopping within buffers, therefore take into account distance decay
  2. Can calculate market share by adding the sum of all flows
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7
Q

What are the 3 outputs of the model?

A

Revenue
Market Share
Flows from origins and destinations

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8
Q

Why is knowing potential revenue important?

A

Gives a benchmark - how does predicted performance compare with actual performance? i.e. overperforming/underperforming
Can also look at revenue per sq. ft. which is a good comparison for comparing the success of stores of different sizes

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9
Q

Why is knowing potential market share important?

A

Stores compete for market share, SIMS show market share at a smaller scale than regional level

BUT important to think of location e.g. 80% market share in a rural area is not the same as 80% market share in an urban area

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10
Q

Why is knowing the flows from origins to destinations important?

A

Good to know where customers are coming from

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11
Q

How do you calculate total revenue and revenue per sq. ft.?

A

Sum of flows coming in from every zone

Revenue per sq. ft.: divide by floorspace

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12
Q

What is the calculation for market share?

A

(revenue of retailer in zone)/(total revenue in zone) x 100

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13
Q

What can be included in ‘j’?

A

Shopping centres
Individual stores
Stores within shopping centres

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14
Q

80% studies use store size as the attractiveness indicator, but what else can be used?

A 
Retail Fascia (Fj) - brand loyalty. Likely to combine Wj and Fj
Price (Pj) - impacts on socioeconomic classes
Site Characteristics (SCj)
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15
Q

What things can be included in site characteristics as an attractiveness indicator?

A

Parking (but likely to be associated with store size)
Key Multiples - stores nearby that are also needed
Ease of Access e.g. bus/car
Visibility - how visible stores are impacts on passing trade
Pedestrian Flows - in shopping centres etc.
Access to other centres

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16
Q

Why is measuring the success of stores within shopping centres more challenging?

A

Have to take into account shopping centre success as well as store success

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17
Q

What did Eyre (1998) create in order to take into account centre attractiveness?

A

Field Survey Attractiveness

Stores given a boost or deboost based on shopping centre success e.g. Meadowhall vs. Bradford SC

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18
Q

What is Fotheringham’s Competing Destination Model (CDM)?

A

Included an extra term Aj to measure ‘distance accessibility’ for zone j
Measures how close each retail outlet is to other retailers
Takes into account store competition
Higher the score, the more attractive the store

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19
Q

Why did Fotheringham make a CDM?

A

Argues that stores near other stores are more attractive than stores with nothing near them
The Aj term is increased if a store has more around it
Used in the SCj characteristic: access to other centres

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20
Q

What is distance decay?

A

When the interaction between origin and destination decreases as the distance between them increases

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21
Q

Why are SIMs better than GIS for measuring distance?

A

GIS uses x, y coordinates thus straight line distance but this is inaccurate
SIMS use a distance/time measure to create a non-linear but exponential relationship

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22
Q

Why is an exponential function better for measuring distance?

A

Controls the rate at which distance decay occurs

Takes into account variations between urban and rural areas

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23
Q

How are urban areas affected by distance?

A

Big relationship between distance and interaction (at the start of the exponential function)

If there are 2 Tesco’s a km apart, no one would travel the extra km for the 2nd Tesco, therefore everyone would go to the first

24
Q

How are rural areas affected by distance?

A

Small relationship between distance and interaction (at the end of the exponential function)

Less provided for in rural areas, therefore if there was a Tesco 9km and one 10km, the extra km becomes negligible, therefore it is assumed nos. at each store will be similar

25
Q

What is the function of beta?

A

Controls for the importance of distance

26
Q

Why does distance vary between consumers?

A 
Car ownership
Amount of spare time
Brand loyalty
Rural vs. urba
Geodemographics - socioeconomic class
Product type i.e. elastic vs. inelastic products
27
Q

How is distance usually measured?

A

Travel time

28
Q

What did Birkin et al. (2010) say was wrong with using travel time as the distance indicator?

A

Assumes everyone has a car

Doesn’t take into account topography e.g. hilly areas would increase time

29
Q

Flows between any demand zone and store will be proportional to:

A
  1. Store attractiveness in relation to attractiveness of competing stores
  2. Accessibility vs, accessibility of competing stores

Ultimately: people want to go to the most attractive and closest store

30
Q

Why do we need to calibrate models?

A

To turn theoretical models into models that reflect reality; need to compare/match model predictions with real observed data

Can access observed data

Want model predictions to be within +/-10% of observed data - most stores want +/-5%

31
Q

Where can observed data be accessed from?

A

Loyalty cards

Consumer surveys

32
Q

What did Wood and Tasker (2008) say about model accuracy?

A

10% variation in sales forecast for a medium store can equate to £5mil. therefore argue for a variation of 5%

33
Q

What do high beta values say about distance?

A

Distance is important - people willing to travel less

34
Q

What do low beta values say about distance?

A

Distance less important - people willing to travel further

35
Q

How does beta get altered in order to give the best representation?

A

If market share is too ‘flat’ then beta is RAISED

If market share is too ‘peaked’ then beta is LOWERED

36
Q

How can geodemographics be used to also alter beta?

A

If an area has a pop. that is not affluent with low car ownership, then distance is important so beta is RAISED

If an area has a pop. that is wealthy with high car ownership, then distance is less important so beta is LOWERED

37
Q

How do we check calibration is accurate?

A

Compare calibrated model predictions with actual observed data

Issue is that unlikely to have a matrix of all flows between each origin and destination, therefore becomes a generalisation - calibrate based on known flows for some consumers and try and ensure it is representative of all consumer

38
Q

What is Average Trip Distance (ATD)?

A

Measures the average distance travelled by consumers

Use of ATD helps get hold of observed data and therefore be able to calibrate beta

39
Q

If model can replicate observed ATD then:

A
  1. Consumer flows likely to be accurate
  2. Store catchments and inflow likely to represent reality
  3. Store revenue predictions should be accurate
40
Q

Why do we disaggregate beta?

A

In reality people have their own beta values based on independent decision making, but can’t do this

Therefore, people are grouped based on shared characteristics based on where they live

41
Q

What is the Standardised Root Mean Standard Error (R2)?

A

SRMSE used to assess model fit
0 = perfect model fit
Want to get R2 as close to 0 as possible

42
Q

What are stores doing to try and increase attractiveness?

A

Tesco: max. floorspace by putting in concession points and other services e.g. cafes

Sainsbury’s: want to capitalise on their unused floorspace by taking over Argos. BUT Argos and Homebase come as a pair - don’t want Homebase

43
Q

What is agglomeration?

A

Grouping non-food stores in retail centres is important as allows for economies of scale
Retail adjacencies can be crucial for attractiveness e.g. importance of anchor stores such as M and S in attracting customers to surrounding stores

44
Q

What did Fotheringham (1986) say about hierarchical choices?

A

SIM assumes straight-forward decision making re. consumer choice in where to shop

He argues that the CDM is better than the SIM as it allows for hierarchical decision making: consumers pick a cluster of retail activity and then decide which stores to visit within the chosen cluster

45
Q

What are economies of agglomeration?

A

Benefits firms gain by locating near each other. Associated with the idea of economies of scale and network effects

46
Q

Why is agglomeration important?

A
  1. Stores located near each other may be more attractive
  2. Enables comparisons between stores
  3. Competing retail centres close to each other may be seen as one destination by consumers, therefore increasing custom
  4. Adjacencies to key anchor stores may also be important
47
Q

What does alpha do?

A

Takes into account store attractiveness

48
Q

How does alpha alter store size to alter attractiveness?

A

Makes a unit of floorspace more or less attractive based on a range of factors e.g. brand

Alpha >1 = more attractive as floorspace increases
Alpha

49
Q

How can alpha be influenced by consumer characteristics?

A

Clarke et al. (2012) argue that consumer characteristics impact on brand e.g. affluent more likely to shop at Waitrose than Tesco

Therefore input ‘m’ (consumer characteristics) after the alpha

50
Q

How does the model adjust for store location and size?

A

Acknowledges that not all affluent customers will go to Waitrose purely because of its brand image if it is small and far away

Therefore, the model recognises that some will pick the nearer and larger Tesco

51
Q

How can we check for model effectiveness?

A

Minimise sum of squares

Want to increase the sum of squares

52
Q

What is Birkin et al.’s (2010) ‘Goodness of Forecast’?

A

Predictive experiments using un-modelled stores

If confident actual trading will be within 1-% of predictions then directors likely to invest

53
Q

Why might the model just not work?

A
  1. Demand misestimations
  2. Not counted all forms of demand e.g. workplace
  3. Not accounted for all existing supply
  4. Product choice important but not in model
  5. Multipurpose trips not accounted for
  6. Calibration data outdated and/or unrepresentative
  7. Mismodelled level of aggregation
  8. Misjudged impact of e-commerce etc.
54
Q

What are ‘objective’ factors in store attractiveness? (BIRKIN ET AL., 2016)

A

Quantifiable factors impacting on store attractiveness e.g. parking facilities, retail adjacencies, relative prices etc.

55
Q

What are ‘subjective’ factors in store attractiveness? (BIRKIN ET AL., 2016)

A

Consumers’ perception of brand image and customer service

ARP (9 decks)

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