Spatial Analysis

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AG2414 Spatial Analysis


Lecturer and Examiner: 

Gyözö Gidofalvi (GG)

Office: TR10A, 308

Office hours: by appointment

E-mail: gyozo(a)

Teaching Assistant:

Appointed annually; in 2021: Haoye Chen haoye(a)


KTH Social:


Spatial analysis is the crux of GIS: the means of adding value to geospatial data, and turning data into information. Therefore, the goals of this course are 1) to teach students the fundamental concepts and advanced techniques of spatial analysis in lectures, 2) to allow students to experience these concepts and apply these techniques to solve realistic model problems in laboratory exercises, and 3) to enforce these concepts and techniques by critically evaluating prominent related scientific articles in seminars. In addition to enforcing concepts and techniques, the laboratory work and seminar will also prepare students to design, undertake, and critically evaluate GIS projects, and the seminars will strengthen the students' public speaking skills as they present scientific articles to their peers and will improve the students' scientific comprehension, design, presentation, and writing skills as they read, summarize, and review the seminar articles.

On the completion of this course, students should be able to:

  • define and explain the unique features of spatial phenomena (spatial autocorrelation, spatial heterogeneity, multiple area unit problem) and argue the importance of spatial modeling

  • describe and differentiate between inductive, deductive, and normative spatial analysis techniques

  • describe, apply, and critically evaluate the process of cartographic modelling and multi-criteria evaluation

  • define and distinguish between a spatial process and spatial pattern

  • explain sources of spatial variation (first and second order effects)

  • define, calculate, and statistically assess centographic, density-based (quadrat counting), and distance-based (mean NN-distance, G, F, K) measures for spatial patterns

  • define spatial autocorrelation and describe and apply methods / measures for its detection (Moran's I, Geary’s C, joins counts), its description and characterization (variogram), and its modelling into sampling, interpolation (Inverse Distance Weighting (IDW), Kriging) and regression (Geographically Weighted Regression (GWR))

  • describe and distinguish between simple and complex systems and outline the top-down vs bottom –up / generalist scientific approach to modelling these systems

  • describe, characterize, apply, analyze, and evaluate Cellular Automata (CA) and Agent-Based Modelling (ABM) for the modeling of complex, time-varying geospatial phenomena

  • define, apply, and evaluate network-based integration / centrality measures to access the accessibility to urban space and its content

  • define common data mining tasks (prediction, classification, clustering, associations, outliers) and describe how standard data mining methods can be extended to treat the space and time dimension in a meaningful way


  • Cartographic modeling & Multi-Criteria Evaluation (MCE)
  • Point pattern analysis
  • Geostatistics, interpolation and Geographically Weighted Regression (GWR)
  • Space syntax and urban morphology
  • Cellular automata and agent-based modeling
  • Spatial, spatio-temporal and trajectory data mining


For admitted students to the Master of Science in Civil Engineering and Urban Management (CSAMH) or the Master of Science in Transport and Geoinformation Technology (TTGTM):

  • AG2429 Geovisualisation (or AG2412 Geovisualisation) or an equivalent course

For other students:

  • A completed bachelor’s degree in civil engineering, urban planning, geomatics, geography, engineering physics, computer science, statistics, economics, and/or mathematics, including at least 6 university credits (hp) in each of the following or their equivalents: Programming, Linear Algebra, Calculus in One Variable, and Probability & Statistics

  • Documented proficiency in English corresponding to English B

  • AG2429 Geovisualisation or equivalent course


LAB2 - Laboratory Work, 3.0 credits, grade scale: P, F

SEM1 - Seminar, 1.5 credits, grade scale: P, F

TEN2 - Examination, 3.0 credits, grade scale: A, B, C, D, E, FX, F


[GA] Geospatial Analysis, Michael J de Smith, Michael F Goodchild and Paul A Longley, 2007, Matador. Free web version at

[GIA] Geographic Information Analysis, David O'Sullivan, David John Unwin, 2010, Wiley.


Students are expected to read the assigned reading before lecture as it is listed under lectures. The readings primarily come from the text books but are complemented by material posted on Bilda.


ArcGIS, Idrisi, QGIS

SCHEDULE (subject to change on agreement)


Students are expected to complete the laboratory exercises in groups of 2. Students should reflect on the laboratory exercises by submitting a report for each of the exercises on Bilda. Reports should be according to the report template that is provided on Bilda and should contain the students’ answers to specific exercise questions and discussions of their results.   


At the end of the course key concepts and techniques will be reinforced in a set of seminars where students will present and discuss scientific articles on the following topics:

  • S1: Cartographic modeling and MCE

  • S2: Geostatistics, interpolation and GWR

  • S3: Cellular automata and agent-based modeling

  • S4: Space and place syntax

  • S5: Spatial data mining

The seminar logistics are as follows:

  • 2 students form a group (same group as in the labs).

  • Each group will present a paper on one topic and act as an opponent on another topic.

  • Logistics of selection and assignment of seminar articles, presenters and opponents at the end of the first lecture.

  • All students should read core literature before the seminar and participate in the discussions during the seminar.

  • Each group is responsible for uploading 1/2 page summary and a 1/2 page review of each of the selected seminar articles before the seminar on the seminar’s Bilda discussion forum.

  • Seminar participations is be evaluated individually.

Article summaries and reviews:

  • Upload your 1/2 page (approx. 1 paragraph) summary and 1/2 page review of each seminar article before the seminar here.

  • One upload per group. In the title / header give the names of the group members. For each article give the title of the article and the summary and review of it in two separate sections. Post in plain text. Use attachments for convenience only if in your review you refer to one or more external articles.

  • The objective of the summary and review is to:

  • Learn to summarize and review scientific research articles

  • Practice academic writing (style and structure)

  • Practice critical thinking

  • Students will need all these skills to conduct a successful MSc thesis project.

  • Do not plagiarize and avoid direct quoting unless it is necessary for reference. Feel free to look at the summaries and reviews of your fellow students to develop your OWN ideas or relate to their reviews.


To encourage continuous student participation bonus points will be assigned to individual students based on their performance in the labs and seminars. In particular, 1 bonus point for will be rewarded to students who finished (submitted and approved) a laboratory exercise before the deadline (maximum of 5 bp), and 1 bonus point will be rewarded to students who actively participate in the discussions of the seminar articles of a given seminar topic to students (maximum of 5 bp). The sum of the bonus points is incorporated in the final course grade of students on top of the exam grade (max 10%).


There will be a final examination (see schedule). Questions will concern the topics covered by the lectures, readings, laboratory exercises, and seminars. A sample exam is posted on Bilda as a study guide. During the exam students are allowed to use:

  1. a printed dictionary and

  2. a single, A4-sized, single-sided sheet of hand-written notes, which must be turned in together with the exam at the end. 


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