社会・環境空間データ分析

1. Spatial autocorrelation and spatial dependence 2. Regression with spatial data 3. Multilevel models and geographically weighted regression 4. Remote sensing with Google Earth Engine 5. Machine learning for socio-environmental data 6. Model evaluation and spatial validation 7. Final examination

This course consists of lectures followed by hands-on exercises in which students practice the techniques covered in class using their own laptops.

Class participation and in-class exercises (30%), two assignments (20% each), and final examination (30%).

<Class Information> This course is a successor to the course Introduction to Geoinformatics and offers advanced content. This course assumes basic knowledge of GIS and RS, as well as familiarity with basic GIS and RS analysis techniques. Students who have not taken Introduction to Geoinformatics may still attend if they have equivalent knowledge and skills. No supplementary instruction on basic GIS or RS knowledge or techniques will be provided. The course will be held in person at the Kashiwa campus (Lecture room on the 7th floor). Students need to bring their laptops. The class uses the GIS software QGIS, which is available on both Windows and Mac. But note that the instructor will use a Windows PC, and the QGIS Windows and Mac versions are not 100% compatible. Mac users may borrow a department Windows PC upon request. Attendance at the first class is mandatory. Students cannot join the course midway. ＜その他＞ 尚，「社会人経験に基づく単位認定」は「『単位認定』の実施要領（2013年9月13日基幹専攻会議改正）」に則り，下記1)および2)の方法で単位認定の可否を判断する． 1) 社会人経験と本講義との関連が記載された履歴書等の資料の精査，及び 2) 本講義のレポート内容に基づく30分程度の口頭試問