Spatial Data Models

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En provenance du cours de Yonsei University

Spatial Data Science and Applications

40 notes

Yonsei University

Spatial Data Science and Applications

40 notes

Spatial (map) is considered as a core infrastructure of modern IT world, which is substantiated by business transactions of major IT companies such as Apple, Google, Microsoft, Amazon, Intel, and Uber, and even motor companies such as Audi, BMW, and Mercedes. Consequently, they are bound to hire more and more spatial data scientists. Based on such business trend, this course is designed to present a firm understanding of spatial data science to the learners, who would have a basic knowledge of data science and data analysis, and eventually to make their expertise differentiated from other nominal data scientists and data analysts. Additionally, this course could make learners realize the value of spatial big data and the power of open source software's to deal with spatial data science problems. This course will start with defining spatial data science and answering why spatial is special from three different perspectives - business, technology, and data in the first week. In the second week, four disciplines related to spatial data science - GIS, DBMS, Data Analytics, and Big Data Systems, and the related open source software's - QGIS, PostgreSQL, PostGIS, R, and Hadoop tools are introduced together. During the third, fourth, and fifth weeks, you will learn the four disciplines one by one from the principle to applications. In the final week, five real world problems and the corresponding solutions are presented with step-by-step procedures in environment of open source software's.

À partir de la leçon

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.

Spatial Data Models9:41

Rencontrer les enseignants

Joon Heo
Professor
School of Civil and Environmental Engineering

With respect to five layers of GIS,

we discussed the first layer,

spatial reference framework in the last lecture.

In this lecture, we'll discuss spatial data model.

How to represent spatial reality in an abstract manner.

The content of the lecture are the following,

comparison of object view and field view,

and related vector model and raster model will be discussed.

And vector model will be investigated in more detail.

I will present other vector models;

network model and TIN model,

and you will learn spatial data models for spatial big data as well in the end.

As mentioned in the first lecture of the week object,

view assumes that space is composed of discrete features such as building,

parcel, road, point of interest and many others.

And they are generally represented in the vector model.

On the other hand, field view assume that space consists of continuous phenomena.

Such as terrain, like rainfall.

And it can be represented in a matrix form of homogeneous grid cells,

which is called raster model.

One exception should be noted that TIN

triangulated irregular network can model a field view of the space.

Which is vector format now the raster model.

The figure on the slide illustrates the examples of object view

and field view of a given spatial reality of land use and land cover.

Here object view is implemented in vector model and field view is in raster model.

Now, let's take a closer look at vector model and raster model.

Vector model has three basic features point, line, polygon.

Vector model can provide three types of data spatial,

attribute, and relationship among spatial features.

It can accommodate more than one attribute for example,

the parcel polygon in green color can have multiple attributes such as owner name,

address, tax assessment, and so on.

Scale and the corresponding level of generalization

determine the quality and the shape of spatial data in vector format.

This issue will be covered in mapping and

Geo-visualization in more detail later in this week.

Data structure is rather complicated than raster model.

Those are the characteristics of vector model.

Raster model has the basic unit, called pixel

and raster model is simply viewed as

2 dimensional array or pixels, just like an image format.

A pixel can have only one attribute value.

So if you need multiple attributes, then additional rater layers are required.

Pixel size defines the spatial resolution, that is, level of detail.

The size of raster data is generally bigger than

vector data and data structure is rather simple,

compatible with simple image formats.

The examples are land cover data in the above, and the digital elevation model in the below.

As mentioned the vector model can support spatial data,

attribute data, and relationship with spatial features.

Spatial data and attribute data can be handled either in a separate manner,

or an integrated manner, everything in DBMS.

After object relational DBMS was developed.

The relationship among spatial components in vector model is called topology.

Which can be maintained for spatial data integrity and efficient spatial data processing.

The concept of topology was already present in the first week.

Topology is defined as a set of rules

that describe spatial relationship among point line and polygon.

You also learned the two main benefits of topology,

data integrity checking and efficient data of operations.

Topological data structure can confirm if spatial data are well composed for example,

polygon is complete with the a crossed loop of lines.

Polygons are non-overlapped.

And space is a fully filled up without any gaps or holes.

Those kind of things can be conformed with topological data structures.

The given set of topological rules on the slide now,

define the relationship among 0D object-node,

1D object-chain,

and 2D object-polygon.

They describe relationship between node and chain,

polygon and chain, polygon and node.

Imagine if we have a data structure supporting

the rules that is being described on the slide.

Then we can speed up basic spatial operation such as adjacency,

connectivity, containment, operations of spatial features.

The figure is a data model,

or point node, line, chain, and Polygon.

That support the topological rules in the previous slide.

Network data model is a vector model with point and line,

which can accommodate all the properties of network.

Network is defined as a group of interconnected things or peoples.

We can easily find samples of network for example,

like as a physical network, subway network, load network,

water supply and sewage network, river network,

internet connection network and on the other hand

as a virtual networks, SNS friend networks.

Network can be represented with graph data structure.

Graph data structure is composed of a set of vertices and edges that connect vertices.

Vertex is a terminal point or an intersection point of edges.

Edge is a link between two vertices.

It can be either one directional or bi-directional.

There are many variation for implementation of graph data structure.

Here I'm presenting one simple method node table and connectivity matrix.

In the connectivity matrix edge can be formed

as matrix element in yellow color now as you can see.

At the upper left corner of physical network let's say maybe subway network,

can be simplified to net mode on the next.

Then it can be implemented with Graph data structure with node table and

corresponding connectivity matrix that you are looking at.

TIN.

TIN is also an important spatial data model in GIS.

TIN stands for triangulated irregular network,

a vector model which can implement of field view of the space.

With contiguous and non-overlapping triangles.

TIN is mainly used for representing terrain and the related analysis.

At the same time, the basic algorithm to build TIN is Delaunay Triangulation,

which is often used in many spatial analysis such as,

proximity analysis for market analysis.

Now let's briefly take a look at spatial data model for spatial big data.

Basically they are mostly equivalent to

conventional spatial data model; either vector or raster data model.

The list of example,

the list explains different spatial data types and corresponding examples.

In the figure smart transportation card transaction and

floating population can be handled with point event data model on the first row,

text trajectory with polyline data model and satellite image in raster data model.

In this lecture you have studied on spatial data models and related topics,

which is second layer of GIS.

At the same time that is basic data models of spatial data science.

All right. This is the end of this lecture. See you in the next lecture.

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