GisMap: GIS TRAINING

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GIS: Methods of analysis / GIS TRAINING

CHAPTER4: Methods of analysis in a GIS

Queries and interrogations

Query, exploration, statistics

Metric measurements and calculations

Metric properties of objects: length or perimeter, surface, etc.
Relationships between objects: distance, orientation

Data transformation

Creating new descriptive attributes
Based on arithmetic, logical, geometric rules

Information synthesis

Scale transfers
Geostatistics and interpolation
Changes in spatial location

Optimization techniques

Optimal locations
Shorter paths, operational research

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Classification methods

Descriptive classification (grouping on a descriptive criterion)

Maintain the form of the distribution
Maintain dispersion: maximize the interclass variance
To reveal the irregularities of the series

Discretization methods (examples)

Classes of the same amplitude
Equal enrolment classes (quantiles)
Use of mean and standard deviation (normal distributions)
Arithmetic or geometric progression
Natural Threshold Method

Examples of methods using localization

Selection of objects on a distance or membership criterion: the creation of buffer zones (or buffer, or mask)
Selection of objects based on orientation or direction criteria
Connecting objects on a criterion of distance or belonging: crossings, hierarchy, aggregation, belonging
Classification by proximity: grouping on a geometric or topological criterion (aggregates)
Proximity and neighborhood operations: geostatistics and interpolation

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Changes in the type of geometric object

From point to point objects: point to the mesh by interpolation, point to the zone by influence (Voronoi) or aggregation, point to the line by creating curves of aggregation
From the objects line to line: line to the zone by expansion or aggregation (weighted by an interaction distance), the line to point (calculation of centroids), the line to mesh by interpolation
From the zone objects: zone to point (centroid), zone to the line (skeletonization), zone to mesh (rasterization)
From mesh objects: mesh to the area (vectorization), mesh to mesh (rasterization and resampling methods)

GIS and remote sensing

Geo-referencing and mosaics

Geometric transformations and photogrammetry
Adjustment and adjustment of values

Which object: point or area?

From pixel to geographical object: processing by zone or by pixel?
The use of semi-joining and aggregation operations

Conventional methods in remote sensing

Different types of satellites, different methods: channels are attributes, indices are methods
Directed and non-directed classifications
Vegetation, building, texture, structure, etc. indices Morphology math.

Urban remote sensing

Aerial photography and orthophoto plans

GIS and interpolation: digital field models

DTM by interpolation

From points or lines by interpolation
Many methods to move from the point to the area: nearest neighbors, inverse distance, Splines, kriging, etc. (deterministic methods vs. probabilistic methods).

DTMs and their methods

Slope, orientation, drains, flows, volumes, visibility, watersheds, etc. Hydrology models.

Representation by illumination, representation in perspective

Distance models, influence models, influence areas

GIS and optimization

Networks and graphs

operational research applications: optimal path
distances along with a network, accessibility problems

Cartography

Cartographic language and graphic semiology

The cartographic language

The components of cartographic language
The elementary graphic signs (point, line, task), the cartographic figurative (built from the elementary signs), the graphic layout (punctual, linear, zonal), the visual variables (shape, size, color, value, orientation, texture-structure, grain).

GIS and mapping

Automatic mapping from a query
Choice of a cartographic projection
Association descriptive attribute - graphics attribute (figurative, layout, visual variables)
Automatic label positioning
Filtering and generalizations

Dressing a map

Graphic scale
North Arrow
Title
Corps Map
Legend
etc...

GIS and the Internet

Software: different organizations

Application and data on a single computer at the customer's premises
The client application and data server over the local area network
Data server and application server over the local area network
Data server and Internet application server, query using an Internet browser

Remote interrogation, dedicated applications

-An "application" organization

-A Client/Server organization

-A server managing the database, responding to requests

-On the customer side, several solutions, for dedicated applications:

CGI
Applet or ActiveX
ASP .Net
JSPX
SVG

An unstable evolving technology.

Available data of uncontrolled quality

Data and metadata: an essential requirement
Quality that is often difficult to assess, data to handle with care, unknown contexts
Extraordinary data servers (USGS, NASA, Google...), but whose free availability is not guaranteed in the long term
Multiple questions on data/information/knowledge ownership

GIS: organization

Project definition and feasibility study

Drafting of specifications describing the objectives and needs of the application.

Evaluation of the necessary data and acquisition flows.

Assessment of the system specifications and objectives in relation to existing systems on the market, to assess the feasibility of the operation and the costs involved.

Final evaluation of the various possible choices in terms of benefits and costs.

Logical organization and functional implementation

General implementation and administrative body (human and financial needs, training and user assistance plans, management of future system evolution based on operating results)

Data acquisition body to manage the various information flows (regular or application-specific flows). This body is responsible for evaluating and describing information sources, access procedures, and acquisition procedures.

Data entry and integration body: structuring, homogenization, validation, coding, coding, entry, control, correction, and integration of data according to the techniques required by the information system.

Data analysis and exploitation body ensuring that user requests and application needs are met according to the specifications.

GIS: Datum and projections / GIS TRAINING

CHAPTER3: Datum and projections

The measurement and representation of the location

The objective of linking to the location requires a common reference frame and known details for the location attribute.

Objects must be georeferenced in the same system.

Datum: the shape and position of the Earth

A reference system, combining:

The definition of a reference form to describe the position of a point by spherical coordinates (longitude, latitude, altitude). This shape is an ellipsoid of revolution.
The position of this ellipsoid in the universe (center and inclination)

Many systems have been defined, independently of each other, by conditions of tangency of the ellipsoid to the Earth's surface at a point

All the coordinates of objects in the same database must be expressed in the same system in order to be comparable

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Projection: represent an ellipsoid on a plane

A cartographic projection is a mathematical operation allowing to represent a portion of the ellipsoid on a plane, by estimating the deformations induced by this operation on curvilinear distances, angles, directions, curvilinear surfaces...

Databases

From the object to the collection of objects: databases

The purpose of the schematization of reality here is to describe not a single object, but a set of objects. It is therefore even more reductive since the context is that of a collection. Attributes must, therefore, be common to all objects in the collection.

A database is an association between a schematization of reality and the objects describing reality according to this schema.

The need for computer management is obvious, to manage all objects in relation to descriptors (attributes), to manage the links between objects and to connect objects to each other. This is managed by a database management system (DBMS).

Database management systems: objectives

Physical and logical independence between data and application programs

Object persistence

Centralized data administration

Optimal management of computer memory and efficient data access

Sharing data between users and managing competing accesses

Reliability, integrity, and consistency of data

Data Security

Interactive queries, declarative data consultation, access to non-computer specialists

The relational model

-A simple description model

Objects are described only by simple type attributes (for example, no attribute in R2 or R3, no recursive definition, no methods)

The set of objects described by the same attributes is called a relationship. Objects are called tuples

All the objects in a relationship can be represented by a table (line=tuple, column = attribute)

The most common DBMS are relational: ACCESS, DBASE, MySQL, ORACLE, etc.

-The tuples are manipulated using relational algebra operators, a formalism that allows the content of the database to be queried:

union
Cartesian product
projection
selection
joint

Relational algebra allows queries to be expressed by a sequence of operators. Expressed in a high-level language, the query ensures the objective of physical independence between data and application program (SQL languages).

The extension of the relational model

Simple and powerful, the relational model only handles data related to the natural order (in the selection and join criteria). It does not allow to properly process data of dimension 2 or more, such as location.

To treat the location of geographical objects with a relational system, it is, therefore, necessary to extend the relational model and algebra for R2 or R3 data. The operations related to this type of data are based on the distance between objects, on ensemblist notions (union, intersection, belonging), on topological notions (adjacency, connectedness), and no longer on a simple order relationship.

Implementation in GIS software

-Management based on descriptive joins

Geometry and topology are stored in separate files

Descriptive information is managed by a traditional DBMS

A unique identifier per object makes the link between geometry and description

Examples:

-Arc view
-ArcInfo

-Management based on an extension of the relationship to localization

Geometry and description are managed together

Two-dimensional indexing is possible

A two-dimensional BD motor (SDE) is used

Examples:

-Sav GIS
-Geodatabase (ArcGIS)
-Spatialware (MapInfo)

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Limitations of the extended model

Hierarchy, belonging, classification by neighborhood... need to introduce methods and types of complex objects (zones, lines, points), hence the de facto extension of the relational model towards object-oriented concepts

Query or consultation operations take the form of real methods and call into question the principle of DBMS: GIS becomes a real application program, and cannot be limited to data management.

A look back at the spatial integrity constraints

Some geometric or topological constraints will always have to be checked (for example, an area must always be closed), as they depend on the logic model or internal description model

Others depend on the semantic definition of the collection (a road network must always be connected, but a telephone network may not be)

-Geometric constraints on arcs

Simplicity (overlapping of an arc on itself)
Extra-simplicity (intersection or duplication of arcs)
Inclusion
Closing up
Connectivity

-Topological constraints of type (area, line, point)

Closing of areas
Belonging of the centroid to its zone
Connection of zones or networks

-Relationship constraints

Unique key constraint
The constraint of belonging to a domain
Neighborhood constraint
Metric constraints

-Joint constraints

Geometric joining constraints: geometric belonging, inclusion (junction of boundaries and hierarchy of relationships), sharing (sharing of arcs between collections),

Descriptive join constraints

geographic information / GIS TRAINING

CHAPTER2: GEOGRAPHIC INFORMATION

Data, information, knowledge

Data:

numbers, text, symbols, usually neutral and independent of context (raw measurements without interpretation)

Information:

differentiated data as dedicated to a subject or subjected to some degree of interpretation

Knowledge:

information interpreted in relation to a particular context, experience, or for a particular purpose

Data or information?

How to understand and represent reality to deal with a computer?
universal vision or contextual vision?
How to define criteria description of reality without a specific problem, to begin with?
Precision, scale, and description, modeling reality: the approach of the geographer.

data models

A data model is a set of rules to represent objects and behaviors of the real world in the logical framework of a computer.

There are four levels of abstraction of reality:

The real world (no abstraction)

The conceptual model (conceptual modeling of reality)

The logic model (organization model of computer-related)

The physical model (an internal organization to the application)

The geographic data

Recording measurements were taken at a certain place at a certain time in the real world

Combines location, time, and descriptive attributes

Difficult to handle in conventional systems data management, which are not equipped to higher dimensional data 1