CSCI 587 • Geospatial Information Management |

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## Reading Materials *** There are no required text books but the following book is recommended as an optional reading material: *[Foundations of Multidimensional and Metric Data Structures by Hanan Samet.](https://www.amazon.com/Foundations-Multidimensional-Structures-Kaufmann-Computer/dp/0123694469/ref=sr_1_1/105-6267729-7062047?ie=UTF8&s=books&qid=1189703688&sr=1-1)* (A 20% discount coupon for the book is available [here](https://www.cs.umd.edu/~hjs/).) Other reading material is based on recently published technical papers available via the ACM/IEEE/Springer digital libraries. All USC students have automatic access to these digital archives. A full list of the related published technical papers can be found [here](./../csci587_syllabus/). ## Additional Policies *** Exam dates are announced on the first week of classes in order to allow students sufficient time to schedule other activities around those dates. Students need to make sure they can take exams on the specified dates and times. There will be no makeup exams. ## Academic Integrity Policy *** All homeworks must be solved and written independently, or you will be penalized for cheating. The USC Student Conduct Code prohibits plagiarism. All USC students are responsible for reading and following the Student Conduct Code. In this course we encourage students to study together. This includes discussing general strategies to be used on individual assignments. However, all work submitted for the class is to be done individually. Some examples of what is not allowed by the conduct code: copying all or part of someone else's work (by hand or by looking at others' files, either secretly or if shown), and submitting it as your own; giving another student in the class a copy of your assignment solution; consulting with another student during an exam. If you have questions about what is allowed, please discuss it with the instructor. ## Academic Conduct *** Students who violate University standards of academic integrity are subject to disciplinary sanctions, including failure in the course and suspension from the University. Since dishonesty in any form harms the individual, other students, and the University, policies on academic integrity will be strictly enforced. We expect you to familiarize yourself with the Academic Integrity guidelines found in the current SCampus. Violations of the Student Conduct Code will be filed with the Office of Student Conduct, and appropriate sanctions will be given. USC seeks to maintain an optimal learning environment. General principles of academic honesty include the concept of respect for the intellectual property of others, the expectation that individual work will be submitted unless otherwise allowed by an instructor, and the obligations both to protect one’s own academic work from misuse by others as well as to avoid using another’s work as one’s own. All students are expected to understand and abide by these principles. Students will be referred to the Office of Student Judicial Affairs and Community Standards for further review, should there be any suspicion of academic dishonesty. The Review process can be found at: [https://sjacs.usc.edu/students/report](https://sjacs.usc.edu/students/report/). Discrimination, sexual assault, and harassment are not tolerated by the University. You are encouraged to report any incidents to [the Office of Equity and Diversity](http://equity.usc.edu) or to [the Department of Public Safety](http://capsnet.usc.edu/department/department-public-safety/online-forms/contact-us). This is important for the safety of the whole USC community. Another member of the university community – such as a friend, classmate, advisor, or faculty member – can help initiate the report, or can initiate the report on behalf of another person. The [Center for Women and Men](http://www.usc.edu/student-affairs/cwm/) provides 24/7 confidential support, and the sexual assault resource center [webpage](http://sarc.usc.edu) describes reporting options and other resources. ## Viterbi School of Engineering Honor Code *** Engineering enables and empowers our ambitions and is integral to our identities. In the Viterbi community, accountability is reflected in all our endeavors. Engineering + Integrity.
Engineering + Responsibility.
Engineering + Community.
Think good. Do better. Be great.

These are the pillars we stand upon as we address the challenges of society and enrich lives. ## Support Systems *** A number of USC’s schools provide support for students who need help with scholarly writing. Check with your advisor or program staff to find out more. Students whose primary language is not English should check with the [American Language Institute](http://dornsife.usc.edu/ali), which sponsors courses and workshops specifically for international graduate students. The [Office of Disability Services and Programs](http://sait.usc.edu/academicsupport/centerprograms/dsp/home_index.html) provides certification for students with disabilities and helps arrange the relevant accommodations. If an officially declared emergency makes travel to campus infeasible, [USC Emergency Information](http://emergency.usc.edu) will provide safety and other updates, including ways in which instruction will be continued by means of blackboard, teleconferencing, and other technology.

Date	Lecture	Readings	Logistics
08/26	Lecture 1 (Arash & Maria): Introduction to Course and Homeworks [ slides ]	Readings 1-6
08/28	Lecture 2 (Arash & Maria): Fundamentals of Computational Geometry [ slides ]	Readings 1-6
09/02	Labor Day, No Class
09/04	Lecture 3 : Spatial Indexes [ slides \| slides (part b) ]	Spatial Data Structures by Hanan Samet	HW1 is released .	HW1 is released . KdTree Material (a), (b), (c)
09/09	Lecture 4 : Spatial Indexes: R-Trees [ slides ]	R-Trees, A dynamic index for spatial searching R+-Trees, A dynamic index for multi-dimensional objects
09/11	Lecture 5 : Nearest Neighbor Queries [ slides ]	Nearest Neighbor Queries
09/16	Lecture 6 : Reverse k-Nearest Neighbor Queries [ slides ]	Reverse kNN Search in Arbitrary Dimensionality
09/18	Lecture 7 : Skyline Queries [ slides ]	An Optimal and Progressive Algorithm for Skyline Queries
09/23	Lecture 8 : Spatial Skyline Queries [ slides ]	The Spatial Skyline Queries Processing Spatial Skyline Queries in Both Vector Spaces and Spatial Network Databases
09/25	Lecture 9 : VoR Trees [ slides ]	VoR-Tree
09/30	Lecture 10 : Spatial Queries: Continuous Nearest Neighbor [ slides ]	Time-Parameterized Queries in Spatio-Temporal Databases	HW2 is released .	HW2 is released .
10/02	Lecture 11 : Spatial Queries: Continuous Nearest Neighbor [ slides ]	Continuous Nearest Neighbor Search
10/07	Lecture 12 : Spatial Queries on non-Euclidean Space: Road Networks [ slides ]	Query Processing in Spatial Network Databases
10/09	Lecture 13 : Spatial Queries on non-Euclidean Space: Road Networks II [ slides ]	Voronoi-Based K Nearest Neighbor Search for Spatial Network Databases Indexing Network Voronoi Diagrams
10/14	Lecture 14 ( Guest Lecturer, Prof. Amr Magdy, UCR ): Geo-Vizualization [ slides ]	AID*; A Spatial Index for Visual Exploration of Geo-Spatial Data.
10/16	Midterm Exam 1
10/21	Lecture 15 : Spatial Queries on non-Euclidean Space: Road Networks III [ slides ]	Scalable Network Distance Browsing in Spatial Databases
10/23	Lecture 16 : Spatial Queries on non-Euclidean Space: Time-dependent Road Network [ slides ]	Online Computation of Fastest Path in Time-Dependent Spatial Networks Efficient K-Nearest Neighbor Search in Time-Dependent Spatial Networks
10/28	Lecture 17 (Maria): Trajectory-based Routing / Reachability Analysis [ slides ]	Time-Dependent Reachability Analysis; A Data-Driven Approach
10/30	Lecture 18 (Arash): Spatio-Temporal Forecasting Tasks [ slides ]	Semi-Supervised Classification with Graph Convolutional Networks Diffusion Convolutional Recurrent Neural Network; Data-driven Traffic Forecasting Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting	HW3 is released .	HW3 is released .
11/04	Lecture 19 : Spatial Crowdsourcing [ slides ]	GeoCrowd; Enabling Query Answering with Spatial Crowdsourcing Maximizing the number of worker's self-selected tasks in spatial crowdsourcing Task matching and scheduling for multiple workers in spatial crowdsourcing Price-aware Real-time Ride-sharing at Scale; An Auction-based Approach
11/06	Lecture 20 : Geo-Social [ slides ]	GEOSO - A Geo-Social Model; From Real-World Co-occurrences to Social Connections EBM; An Entropy-based Model to Infer Social Strength from Spatiotemporal Data A General Framework for Geo-Social Query Processing Real-Time Multi-Criteria Social Graph Partitioning; A Game Theoretic Approach
11/11	Veterans Day, No Class
11/13	Lecture 21 : Location Privacy [ slides ]	Providing K–Anonymity in Location Based Services Geo-indistinguishability, A Principled Approach to Location Privacy A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy A Neural Database for Diferentially Private Spatial Range Queries
11/18	Lecture 22 ( Guest Lecturer, Prof. Ibrahim Sabek, USC ): Adopting Markov Logic Networks for Big Spatial Data and Applications [ slides ]	Sya; Enabling Spatial Awareness inside Probabilistic Knowledge Base Construction. TurboReg; A Framework for Scaling Up Spatial Logistic Regression Models.
11/20	Lecture 23 : Geo Privacy [ slides ]	walk2friends; Inferring Social Links from Mobility Profiles Protecting against Inference Attacks on Co-Location Data Privacy-Preserving Online Task Assignment in Spatial Crowdsourcing with Untrusted Server
11/25	Lecture 24 : Trajectory Mining and Moving Behavior Analysis [ slides ]	VAMBC; A Variational Approach for Mobility Behavior Clustering CSGAN; Modality-Aware Trajectory Generation via Clustering-based Sequence GAN Generating Realistic and Representative Trajectories with Mobility Behavior Clustering
11/27	Thanksgiving Break, No Class
12/02	Lecture 25 (Kate): Geo-Foundation Models and Large Location Models (LLMs) [ slides ]	On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper) Hierarchical Transformer with Spatio-temporal Context Aggregation for Next Point-of-Interest Recommendation TrajGPT; Controlled Synthetic Trajectory Generation Using a Multitask Transformer-Based Spatiotemporal Model
12/04	Midterm Exam 2

CSCI 587 • Geospatial Information Management

Course Description

Grading

Lectures