Guy Ndjeng GIS

Laws of Location science

Some locations are better than others for a given purpose and Efficient locations tend to beat inefficient ones.

Spatial context can alter site efficiencies.

Pure exploration.

It’s hard to find certain things in DC.

Programming (LBS)

Methodological

Reuse network analysis methods with real data

Empirical

Based on the data we have, we are going to make suggestions on shopping centers site location without guaranteeing that we can validate/verify our results (approximation).

Problem: Given the spatial distribution of shopping centers in Washington D.C, suggest a potential location for a new shopping that will offer both high/low products.

“High/Low” goods shopping centers.

Didn’t consider the whole of DC.

Data we used are 3 years old.

Results will not be optimized.

Validation/ Verification.

Accuracy / Approximation / Precision.

Optimization.

Environmental Impact.

Network Datasets

Service Areas

Spatial Joins

OD Cost Matrix

Site Analysis Matrix for Owners within facility 2 Count: 1383 Minimum: 4521.069923 Maximum: 5943.823062 Sum: 7173827.519801 Mean: 5187.149327 Standard Deviation: 293.283617 Site2 Analysis Matrix for Owners within facility 2 Count: 1383 Minimum: 4373.933307 Maximum: 6164.668041 Sum: 7161478.935235 Mean: 5178.220488 Standard Deviation: 418.342496

Site location facilities problem do not have exact solutions.

Necessary for finding best/most suitable locations

Requires a combination of mathematical expressions/models/verification/validations algorithms.

D.C Demographics, http://en.wikipedia.org/wiki/Washington,_D.C.

A study of Access to healthcare centers in rural china, Evidence

from GIS Network Analysis, (http://www.chinapovmap.org/NR/rdonlyres/ennerdhljj4tqavpu7tapznr5zpk3obiezahk5oox3lilcnmyq3mux3qyawz6emysxiva43d2hdu

3p/GeographicAccesstoHealthCentersruralShaanxi.pdf)

ArcGIS Buses in Success (http://www.esri.com/industries/public_transit/index.html)