Encyclopedia topics
Nupur Bhatnagar
Spatial storage and indexing:
The R-tree and the quad tree are commonly used multi-dimensional index
structures in spatial databases. R-tree uses minimum bounding rectangles and
operations like insertion, deletion, and searching are based on the position of
MBR’s.I n this article I would write the different storage structures and space
filling curves that are supported by the spatial databases. I would also discuss
index structures including R tree family and grids. At the near end of the article I
would like to mention some of the interesting properties of the storage and
indexing structures.
Key Words: range queries, data clustering, Hilbert curve, space filling curves.RTree family, Quad tree

Spatial Database: A tour – Shashi Shekhar, Sanjaya Chawala
The spatial database book provides a detailed and comprehensive explanation of
Spatial Storage along with the SDBMS physical model and spatial data and
operations. The author has also discussed common spatial Queries and
Operation along with description of common file structure and Space filling
curves like Z curve and Hilbert curve. Later in the chapter there is a
comprehensive description of Index structures like grid files and R tree family.

Wikipedia:
www.wikipedia.org/
It gives a easy to understand description about Spatial indexes and spatial index
methods like Grid, R-tree and its variants like R* tree and R+ tree. It also
discusses quad trees-the region quad tree point quad tree , edge quad tree. It
also contains a description of search and insertion algorithms.
Interesting topics related to “Spatial storage and indexing”
Space filling Curves:

Analysis of the Clustering Properties of the Hilbert Space-Filling Curve.
http://www.spatial.cs.umn.edu/CS8715/SA2_moon_jagadish_faloutsos_saltz.pdf
This paper exploits the clustering property of the Hilbert space-filling of different
shapes like polygons,polyhedras.It states the clustering property of the Hilbert
space-filling curve as a linear mapping of a multidimensional space.
There is also a performance comparison between z curve and Hilbert curve wrt
to the clustering property.

Object-Based Selective Materialization for Efficient Implementation
of Spatial Data Cubes.
www-faculty.cs.uiuc.edu/~hanj/pdf/tkde00.pdf
Key Words: Data warehouse, data mining, online analytical processing (OLAP),
spatial databases, spatial data analysis
This paper highlights the spatial OLAP, spatial data warehouse models, spatial
cubes. The author has proposed a new terminology
“Object-based selective materialization” that exploits the granularity at the level
of a single cell of a cuboid.
Spatial Data Mining:
Key Words: Spatial Data Mining, Classification. Clustering, Spatial
Autocorrelation, , Spatial Outliers, Hot Spots, collocation mining
Spatial Data Mining is a process of finding interesting hidden pattern in large
spatial database. Examples include population census data, weather data. The
process involves many phases that includes data cleaning, data pre-processing,
feature selection, model building, evaluating a model. This article would revolve
around the need of spatial data mining, different classification techniques –
Spatial regression, Association rule mining, Clustering algorithms and outlier
detection. The different key sources that I would refer are:

Spatial Database: A tour – Shashi Shekhar ,Sanjaya Chawala
This book provides a in depth discussion of concepts of pattern and spatial data
mining. It motivates the readers about the interesting features of SDM explaining
data mining process and novel spatial data mining techniques. The book covers
topics like families of SDM including Location prediction, Spatial interaction, Hot
spots. It also explains clustering, Auto correlation, spatial auto regression,
classification, Association rule techniques, outlier detection technique and
mapping SDM pattern families to spatial patterns.

http://www-cse.uta.edu/~cook/dm/lectures/l13/index.htm
It the course website of university of Texas at Arlington. It has a decent material
giving an overview of spatial data mining and few interesting examples like
weather pattern analysis. It describes Spatial OLAP, Spatial Associations and
Hierarchy of Spatial Relationships.
Coming along the way of SDM algorithms it discusses Spatial Classification,
Spatial prediction and trend analysis, spatial primitives, spatial trend detection.
Some special topics like time-series data mining, mining surprising temporal
pattern are also useful.
Some Articles ,tutorials and special topics:

www.giscience.org/GIScience2000/papers/232-Peuquet.pdf
This is an interesting article named Mining Spatial data using an interactive rule
based approach. The author tries to integrate C4.5 algorithm with geographic
visualization capabilities. It discusses the need of the the geographic
visualization capabilities to become an integral part of the data mining process.

http://www.spatial.cs.umn.edu/sdm.html
This tutorial is a subset of topics covers in the Spatial database books but still its
relevant to read as it highlights some important details of spatial data types,
spatial relationships, and spatial autocorrelation and introduces spatial data
mining in the following categories: location prediction, spatial outlier detection,
and co-location mining.

http://www.spatial.cs.umn.edu/CS8715/SDM9_cao.pdf
Mining Frequent Spatio-temporal Sequential Patterns:
The movement of mobile objects can be represented as a sequence of
timestampped locations. This paper discusses an innovative algorithm to find
patterns by a improved version of Apriori Algorithm.