空間情報解析

Week 1 (10/3): Introduction to spatial analysis What is spatial data? Characteristics of spatial data, etc. Week 2: (10/10): Descriptive spatial statistics Measures of central tendency, measures of dispersion, location quotient, etc.. Week 3 (10/17): Analysis of point patterns 1 CSR, quadrat analysis, NND, kernel density estimation Week 4 (10/24): Analysis of point patterns 2 [Online] K-function, cross K-function, colocation quotient Week 5 (10/31): Spatial pattern in areal data [Online] Spatial autocorrelation, Moran’s I, Geary’s c Week 6 (11/7): Cluster detection in spatial data Local Moran’s I, Getis and Ord’s G, spatial scan statistic Week 7 (11/12): Spatial clustering SCHC, DBSCAN, SKATER, REDCAP, etc. Week 8 (11/28): Spatial interpolation Nearest Neighbor interpolation, inverse distance weighted, kriging Week 9 (12/5): Spatial interaction model Gravity model, entropy model, OD flows Week 10 (12/12): Spatial regression model 1: considering spatial autocorrelation Classic approaches including spatial econometric, geostatistical, and conditional autoregressive modeling Week 11 (12/19): Spatial regression model 2: considering spatial heterogeneity Spatially varying coefficient modeling Week 12 (1/9): Spatial compositional data analysis Compositional data, log-ratio analysis, constant-sum constraint Week 13 (1/23): Student presentation or preparation for student presentation * We may set the presentation preparation day based on the number of enrolled students. Week 14 (1/30): Student presentation

This course will be taught in English. Lectures will primarily be held in person on campus. In addition to the lectures, students are expected to study research articles utilizing spatial statistical methodologies.

Grade evaluation will be based on (1) an in-class presentation and (2) a written report, both about a literature review of research articles utilizing spatial statistical methodologies. Requirements for the in-class presentation and report will be explained during the lectures. Class participation will also be taken into consideration. Plagiarism is strictly prohibited. All final work presented and submitted to the instructors must be the original and independent effort of each student. Failure to comply will result in no credit being awarded.

Not specified.

Anselin, L. 2024. An Introduction to Spatial Data Science with GeoDa: Volume 1: Exploring Spatial Data. Chapman and Hall/CRC Press. Bailey, T.C. and Gatrell, A.C. 1995. Interactive Spatial Data Analysis. Prentice Hall. Pebesma, E. and Bivand, R. 2023. Spatial Data Science: With Applications in R. Chapman and Hall/CRC Press. Rogerson, P.A. and Yamada, I. 2009. Statistical Detection and Surveillance of Geographic Clusters. CRC Press.

Prerequisites: Students are supposed to have basic knowledge of statistics (e.g., having successfully completed an introductory statistics course in their undergraduate training). Some knowledge of spatial information science and/or geographic information systems (GIS) is preferable, but not required.