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Location, Transport and Land-use : Modelling Spatial-Temporal Information. Yupo Chan, PhD PE Professor & Founding Chair Department of Systems Engineering University of Arkansas at Little Rock. Underlying Principles for. Siting Facility location

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Location transport and land use modelling spatial temporal information l.jpg

Location, Transport and Land-use: Modelling Spatial-Temporal Information

Yupo Chan, PhD PE

Professor & Founding Chair

Department of Systems Engineering

University of Arkansas at Little Rock


Underlying principles for l.jpg

Underlying Principles for

  • Siting

    Facility location

    Competitive allocation of products & service

  • Product/service delivery

    Location-routing

  • Community development

    Land-use planning

    Spatial forecasting


When asked about the three most important factors for fast food success l.jpg

When asked about the three most important factors for fast-food success,

McDonald's founder :

"Location, location, location.”

E-Commerce:

Location, price, service


Extremal solution l.jpg

Extremal Solution

  • Network facility-location models

  • Nodal-optimality property

  • Extremal conditions also exist in planar location models


Slide5 l.jpg

Solutions to 3-city configuration


Slide6 l.jpg

Cost Functions of Distance

cij = dij


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Image Processing Using p-medoid Method

Original Picture (from GOES satellite IR2 channel)


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(a) Raw image

(b) Spectral pattern- recognition (w=0)

(c) Spectral and spatial pattern-recognition (0.5<w< 1.0)

Legend

* Representative

pixel

Contextual image-classification using p-medoid method


Result l.jpg

Result

Classification using p-medoid (3 classes)

W=0.5


Slide10 l.jpg

3-dimensional Space-filling Curve

z

j

i

Y

X

Legend

demands

facility

i, i´

j


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1

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iHospitalXiLatitudeYiLongitudeZi Patients

1Charlotte35.2180.4400.03125

2Ft Gordon33.3781.9739 0.8125

3Ft Bragg35.1779.02234 0.8594

4Ft Jackson33.9481.1244 0.9063

5Charleston SC32.9080.0429 0.9531

Medical-evacuation Problem


Slide12 l.jpg

zij= lateral re-supply from node i to j


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SOLUTIONS: A COMPARISON


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Braess-paradox Game


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Spatial Location & Allocation

  • Gaming

  • Generalized transportation model

    – Includes regional input-outputs

  • Equilibrium vs. Disequilibrium

    –Generalized multi-regional growth equilibria

  • Entropy

    –freq. with which an event occurs

  • Entropy maximization

    –to capture all possible patterns (information-minimization or spatial uncertainty principle)


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Legend

WiFacility in zone i

pi Price of goods and services at zone i

ri Land rent in zone i

A probable configuration of zonal activities


Slide17 l.jpg

Facility

stock at

zone 1

W3(t)

W2(t)

W1(t)

W4(t)

Facility

stock at

zone 2

0.22–

0.2–

0.4–

0.17–

0.26–

0.3–

0.29 –

0.12 –

0.2 –

Facility

stock at

zone 3 & 4

time t

time t

time t

0

0

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|

2.5

|

2.5

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|

7.5

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7.5

< 14

= 70

= 70

 = 0.35

 = 14

 = 70

 = 14

 = 0.35

Responses to a New Shopping Center in zone 2


Slide18 l.jpg

Dallas

1 2 3 4 . . .

STUDY AREA

San Antonio

Houston

. . . 398 399 400

Pixel map of Texas Gulf Coast


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Single Pixel NVI Forecast Series


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Spatial-Temporal Canonical-Analysis


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Random or Poisson Field

  • Backshift operator, lag operator, image-processing mask, & spatial location/allocation

    All based on a weight matrix

  • Homoscedasticity, stationarity, homogeneity

    If the correlation parameters are finite, the derived local averaging field become a continuous parameter Gaussian field.

  • Ergodicity and isotropy

    A useful property & through proper local-averaging, such properties can often be obtained


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Emerging Techniquesfor

  • Emergency-response to natural and manmade hazards

  • Supply-chain management

  • Intelligent transportation systems

  • Real-estate development

  • Urban land-use plans

  • Satellite remote-sensing

  • Environmental planning

  • Infrastructure management


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