À propos de ce cours
4.4
79 notes
25 avis
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire

Niveau intermédiaire

Heures pour terminer

Approx. 12 heures pour terminer

Recommandé : 7 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais

Compétences que vous acquerrez

Spatial AnalysisQgisBig DataGeographic Information System (GIS)
100 % en ligne

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire

Niveau intermédiaire

Heures pour terminer

Approx. 12 heures pour terminer

Recommandé : 7 hours/week...
Langues disponibles

Anglais

Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1
Heures pour terminer
1 heure pour terminer

Understanding Spatial Data Science

The first module of "Spatial Data Science and Applications" is entitled to "Understanding of Spatial Data Science." This module is composed of four lectures. The first lecture "Introduction to spatial data science" was designed to give learners a solid concept of spatial data science in comparison with science, data science, and spatial data science. For Learner's better understanding, examples of spatial data science problems are also presented. The second, third, and fourth lectures focuses on "what is spatial special? - unique aspects of spatial data science from three perspectives of business, technology, and data, respectively. In the second lecture, learners will learn five reasons why major IT companies are serious about spatial data, in other words, maps. The third lecture will allow learners to understand four issues of dealing with spatial data, including DBMS problems, topology, spatial indexing, and spatial big data problems. The fourth lecture will allow learners to understand another four issues of spatial data including spatial autocorrelation, map projection, uncertainty, and modifiable areal unit problem....
Reading
5 vidéos (Total 45 min), 1 quiz
Video5 vidéos
1.1 Introduction to Spatial Data Science11 min
1.2 Why is Spatial Special? (I) - A Business Perspective12 min
1.3 Why is Spatial Special? (II) - A Technical Perspective9 min
1.4 Why is Spatial Special? (III) - A Data Perspective8 min
Quiz1 exercice pour s'entraîner
Understanding Spatial Data Science10 min
Semaine
2
Heures pour terminer
1 heure pour terminer

Solution Structures of Spatial Data Science Problems

The second module is entitled to "Solution Structures of Spatial Data Science Problems", which is composed of four lectures and will give learners an overview of academic subjects, software tools, and their combinations for the solution structures of spatial data science problems. The first lecture, "Four Disciplines for Spatial Data Science and Applications" will introduce four academic disciplines related to spatial data science, which are Geographic Information System (GIS), Database Management System (DBMS), Data Analytics, and Big Data Systems. The second lecture "Open Source Software's" will introduce open source software's in the four related disciplines, QGIS for GIS, PostgreSQL and PostGIS for DBMS, R for Data Analytics, Hadoop and Hadoop-based solutions for Big Data System, which will be used throughout this course. The third lecture "Spatial Data Science Problems" will present six solution structures, which are different combinations of GIS, DBMS, Data Analytics, and Big Data Systems. The solution structures are related to the characteristics of given problems, which are the data size, the number of users, level of analysis, and main focus of problems. The fourth lecture "Spatial Data vs. Spatial Big Data" will make learner have a solid understanding of spatial data and spatial big data in terms of similarity and differences. Additionally, the value of spatial big data will be discussed....
Reading
4 vidéos (Total 46 min), 2 lectures, 1 quiz
Video4 vidéos
Open Source Software's7 min
Spatial Data Science Problems15 min
Spatial Data vs. Spatial Big Data9 min
Reading2 lectures
QGIS vs. ArcGIS10 min
What is spatial Big Data?10 min
Quiz1 exercice pour s'entraîner
Solution Structures of Spatial Data Science Problems10 min
Semaine
3
Heures pour terminer
2 heures pour terminer

Geographic Information System (GIS)

The third module is "Geographic Information System (GIS)", which is one of the four disciplines for spatial data science. GIS has five layers, which are spatial reference framework, spatial data model, spatial data acquisition systems, spatial data analysis, and geo-visualization. This module is composed of six lecture. The first lecture "Five Layers of GIS" is an introduction to the third module. The rest of the lectures will cover the five layers of GIS, one by one. The second lecture "Spatial Reference Framework" will make learners understand, first, a series of formulation steps of physical earth, geoid, ellipsoid, datum, and map projections, second, coordinate transformation between different map projections. The third lecture "Spatial Data Models" will teach learners how to represent spatial reality in two spatial data models - vector model and raster model. The fourth lecture "Spatial Data Acquisition Systems" will cover topics on how and where to acquire spatial data and how to produce your own spatial data. The fifth lecture "Spatial Data Analysis", will make learners to have brief taste of how to extract useful and valuable information from spatial data. More advanced algorithms for spatial analysis will be covered in the fifth module. In the sixth lecture "Geovisualization and Information Delivery", learners will understand powerful aspects as well as negative potentials of cartographic representations as a communication media of spatial phenomenon. ...
Reading
6 vidéos (Total 82 min), 2 lectures, 1 quiz
Video6 vidéos
Spatial Reference Framework22 min
Spatial Data Models9 min
Spatial Data Acquisition15 min
Spatial Data Analysis11 min
Geo-visualization and Information Delivery14 min
Reading2 lectures
Sources of Spatial Data10 min
Making Sense of Maps10 min
Quiz1 exercice pour s'entraîner
Geographic Information System (GIS)20 min
Semaine
4
Heures pour terminer
2 heures pour terminer

Spatial DBMS and Big Data Systems

The fourth module is entitled to "Spatial DBMS and Big Data Systems", which covers two disciplines related to spatial data science, and will make learners understand how to use DBMS and Big Data Systems to manage spatial data and spatial big data. This module is composed of six lectures. The first two lectures will cover DBMS and Spatial DBMS, and the rest of the lectures will cover Big Data Systems. The first lecture "Database Management System (DBMS)" will introduce powerful functionalities of DBMS and related features, and limitations of conventional Relational DBMS for spatial data. The second lecture "Spatial DBMS" focuses on the difference of spatial DBMS from conventional DBMS, and new features to manage spatial data. The third lecture will give learners a brief overview of Big Data Systems and the current paradigm - MapReduce. The fourth lecture will cover Hadoop MapReduce, Hadoop Distributed File System (HDFS), Hadoop YARN, as an implementation of MapReduce paradigm, and also will present the first example of spatial big data processing using Hadoop MapReduce. The fifth lecture will introduce Hadoop ecosystem and show how to utilize Hadoop tools such as Hive, Pig, Sqoop, and HBase for spatial big data processing. The last lecture "Spatial Big Data System" will introduce two Hadoop tools for spatial big data - Spatial Hadoop and GIS Tools for Hadoop, and review their pros and cons for spatial big data management and processing. ...
Reading
6 vidéos (Total 79 min), 1 lecture, 1 quiz
Video6 vidéos
Spatial Database Management System (SDBMS)14 min
Big Data System – MapReduce13 min
Big Data System – Hadoop11 min
Hadoop Ecosystem11 min
Spatial Big Data Systems12 min
Reading1 lecture
DBMS vs. MapReduce10 min
Quiz1 exercice pour s'entraîner
Spatial DBMS and Big Data Systems20 min

Enseignant

Avatar

Joon Heo

Professor
School of Civil and Environmental Engineering

À propos de Yonsei University

Yonsei University was established in 1885 and is the oldest private university in Korea. Yonsei’s main campus is situated minutes away from the economic, political, and cultural centers of Seoul’s metropolitan downtown. Yonsei has 3,500 eminent faculty members who are conducting cutting-edge research across all academic disciplines. There are 18 graduate schools, 22 colleges and 133 subsidiary institutions hosting a selective pool of students from around the world. Yonsei is proud of its history and reputation as a leading institution of higher education and research in Asia....

Foire Aux Questions

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