Syllabus

Title
0802 Location Analytics and Geospatial Data 1 (LAGD 1)
Instructors
Florian Martin, MSc., Ass.Prof. Mag.Dr. Petra Staufer-Steinnocher
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/21/18 to 09/28/18
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Wednesday 10/03/18 01:30 PM - 05:00 PM D2.0.031 Workstation-Raum
Wednesday 10/10/18 01:30 PM - 05:00 PM D4.1.212 GIS Lab
Wednesday 10/17/18 01:30 PM - 05:00 PM D4.1.212 GIS Lab
Wednesday 10/24/18 01:30 PM - 05:00 PM D4.1.212 GIS Lab
Wednesday 11/07/18 01:30 PM - 05:00 PM D4.1.212 GIS Lab
Wednesday 11/14/18 12:30 PM - 03:00 PM D4.1.212 GIS Lab
Wednesday 11/21/18 01:30 PM - 05:00 PM D4.1.212 GIS Lab
Contents
This course provides an advanced introduction to state of the art network-based transportation and supply chain modeling with a specific focus on recent developments in
  • exploratory geospatial analysis
  • location/allocation analysis
  • cloud-based services and open-source data infrastructures & tools

We will use real-world data and case studies in various industries for

  • Locating facilities, e.g., a new warehouse of a major retail chain, a new hub/spoke in a distribution network
  • Allocating, e.g., customers to retail/service outlets, regional warehouses to a central warehouse, resources to production sites
  • Evaluating , e.g., service/infrastructure networks and customer potentials, trade area and distribution/production network (re-)design, geospatial risks and sustainability effects

The topics are addressed from a methodological-theoretical as well as an empirical perspective, both with a particular emphasis on spatial aspects. Considerable attention will be paid to gaining hands-on experience in the application of spatial analysis techniques of events that occur on and alongside networks in empirical practice, using spatial analytics methods and tools like ArcGIS Desktop and ArcGIS Online as well as Open-Source Software like GeoDa, CrimeStat, GWR or SANET.

Learning outcomes

Students learn selected theoretical and empirical methods and get a good understanding of the fundamental questions that are addressed in the context of SCM, the methods with which these are addressed, and the current state of affairs in the literature.

By the end of this course students 

  • possess a relevant background and a good mastery of models, methods and techniques used in the domain
  • have the ability to select and apply appropriate modeling tools in specific decision making contexts
Attendance requirements

According to the examination regulation full attendance is intended for a PI. Absence in one unit is tolerated if a proper reason is given.

Teaching/learning method(s)
  • Lecture and discussion
  • papers to read
  • lab course tutorials
  • assignments
Assessment

Tasks (max. achievable points = 100)

  • readings and assignments (45)
  • active participation in class discussion (5)
  • final exam (50)
 

Grading scale:

(1) Excellent: 90% - 100%

(2) Good: 80% - <90%

(3) Satisfactory: 70% - <80%

(4) Sufficient: 60% - <70%

(5) Fail: <60%

Prerequisite for passing the course: minimum performance of 40% in the final examination.

Prerequisites for participation and waiting lists

For incoming exchange students: 10 ECTS in Supply Chain Planning, Global Supply Chain Design, Transport/Logistics (Network) Management/Analysis/Planning, Operations Research, or Geographic Information Systems and Analysis, Spatial Business Intelligence/Location Analytics.

Recommended previous knowledge and skills

Participation is restricted to MSc SCM students.

Availability of lecturer(s)

Office hours: please, send email to petra.staufer-steinnocher@wu.ac.at to make an appointment

Other
Course material: There is no traditional course text. But the lecture slides and a limited number of readings are provided on the learn@wu platform.
Unit details
Unit Date Contents
1 10/03/18

    Lecture and discussion

    • Introduction to LAGD Location Analytics and Geospatial Data
    • Exploratory geospatial analysis in SCM

    Learning materials for Unit 1: Slides Course Intro and Exploratory Spatial Data Analysis, Slides: KDE as a method for describing spatio-temporal changes food retailing market

    Computer lab

    • hands-on exploratory geospatial analysis using
    • open & licensed tools (ArcGIS, CrimeStat, GeoDa, GWR),
    • case studies

     

        2 10/10/18

        Hands-on exploratory spatial data analysis in ArcGIS and Extensions

        • global and local spatial autocorrelation
        • hotspots in point patterns, kernel density
        • case studies
        3 10/17/18

        Lecture and discussion Location-Allocation-Models in SCM

        Please read carefully to be prepared for this unit and for the next lab-unit:

        • for overview and introduction read Church, RL(2005): Location modeling and GIS, in Longley, PA, Goodchild, MF, Maguire, DJand Rhind, DW (eds): Geographical Information Systems: Principles, Techniques,Management and Applications [Chapter 20, on CD-ROM]
        • some more specifications are provided in Miller, H.J. and Shaw, S.-L. (2001): Geographic Information Systems for Transportation: Principles and Applications, pp. 199-213 [Chapter 6: Network Flows and Facility Location; facility location within networks part only]. New York: Oxford University Press

        Download learning materials

        More hands-on ESDA and Location-Allocation-Modeling in ArcGIS and Extensions

        • hotspots in point patterns, kernel density
        • location-allocation
        • case studies

         

        4 10/24/18

        Lecture and discussion Location Analytics in the Cloud 1: Foundations and Applications

        • LA/LI terms, definitions, explanations
        • Cloud computing
        • The GEO Web
          • Distributing Data
          • The Mobile User
          • Virtual Reality and Augmented Reality
          • Location Based Services
        • Distributing the Software: GIServices
        • Service Oriented Architecture
        • Prospects

        Download learning materials: Slides Location Analytics in the Cloud

        ecture and discussion Location Analytics in the Cloud 2: Geospatial Data

        • (semi-)open geospatial data incl. sensor network data
        • sharing/publishing in SC business environments

        Download learning materials: Slides Geospatial Data in the Cloud (to be updated some days before class)

        5 11/07/18

        Hands-on LAGD on desktop and in the Cloud

        • Location-allocation, batch processing and model builder
        • ArcGIS Online
        6 11/14/18

        Hands-on LAGD in the Cloud: Creating and publishing Web Services

        7 11/21/18

        Written exam: models and methods

        Last edited: 2018-10-11



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