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Management of Spatial Information

Spatial Information Management

Learners’ Notes

© FAO - ITC 2013

Course: Management of Spatial Information Unit 1: Introduction to Management of Spatial Information

Lesson 1: Spatial Information Management

Learners’ Notes 1

Table of contents Learning objectives ................................................................................................. 3

Introduction ........................................................................................................... 3

Spatial data and information ................................................................................... 3

Spatial problem ...................................................................................................... 4

Spatial problem - how to get from A to B? ............................................................... 4

Key methodological elements in solving spatial problems .......................................... 6

Data ...................................................................................................................... 7

Functionality .......................................................................................................... 7

Visualization ........................................................................................................... 9

The process of spatial information management ....................................................... 9

Standards ............................................................................................................ 11

Spatial information working environment ............................................................... 11

Spatial information systems .................................................................................. 12

Spatial data acquisition - primary and secondary data ............................................. 13

Spatial data acquisition – Mobile GIS ..................................................................... 15

Spatial data acquisition – Remote sensing .............................................................. 15

Spatial data acquisition – RS techniques and products ............................................ 15

Spatial data acquisition – integration of different sources ........................................ 16

Spatial analysis – Data integration ......................................................................... 16

Spatial analysis – Modeling ................................................................................... 17

Spatial analysis– Error propagation ........................................................................ 17



Learners’ Notes 2

Spatial data reporting ........................................................................................... 17

Summary ............................................................................................................. 18



Learners’ Notes 3

Learning objectives

At the end of this lesson, you will be able to:

• describe the fundamentals of spatial data;

• understand the basics of spatial information in the organization;

• describe the operational use of spatial information;

• understand how this module can help you in the management and manipulation of spatial

data.

Introduction

This module gives a comprehensive overview of the nature and characteristics of spatial data:

• how it can help improve the quality and scope of activities in organizational processes;

• how to manage the infrastructure internally; and

• how to integrate the infrastructure in a wider context of data acquisitioning and

provisioning.

This first lesson of the module provides a general overview of spatial information management, and

focuses on two main aspects: organization and operational use of spatial information.

Spatial data and information

Spatial data (given facts and representations that can be operated upon by a computer) are given

facts about space that can be operated upon by a computer. For this module, the only relevant

space is the geographic space, (space in which locations are defined relative to the Earth’s

surface. This is the usual space geographic information systems work with) in which, by spatial

data we mean data that contains positional values, such as x, y or latitude, longitude coordinates.

However, humans work with and act upon information (data that has been interpreted by a

human being), not data. Spatial information is spatial data interpreted by a human being.

Human perception and mental processing leads to information, and hopefully, to understanding and

knowledge.

Lessons 1.2, 1.3 and 1.4 provide deeper insight into the fundamentals of spatial data, spatial

information and spatial data types.



Learners’ Notes 4

Having spatial data is essential for acquiring information about our world.

We humans, strive for this by answering all kinds of questions about geo-space such as way-

finding and land-use classification, but also tasks like assessing the impact of natural

hazards on people’s lives.

In Lesson 1.2 you will see an example of the impact of El Niño/La Niña events and ways how

spatial information can help in interpreting what were the impacts of these events.

Spatial problem

On the next screens, you will have a look at a simple example of a spatial problem. The aim is to

emphasize the different considerations that a spatial information manager needs to take into

account.

Spatial problem is a situation in which information needs to be derived to support decision-

making, and in that derivation the spatial embedding of the model elements cannot be ignored.

Geo-related problems need data, computations and visuals that are spatially explicit.

Spatial problem - how to get from A to B?

How to get from A to B? It is a typical geo-related problem? The question is too general and is in

need of further specification. Following you can see some useful questions you should ask, as

spatial information manager, depending on the information you want to know:

What you need to know What you need to ask

Specific information How many kilometers is it from A to B?

A deeper insight to optimize your travel What is the optimal route from A to B?

The impact of travelling to the citizens of A and B How does the time of day influence

travel time?

More information for a broader decision making

process on urban and regional planning (involving

discussion on economics and politics)

How to solve traffic jams?



Learners’ Notes 5

To obtain answers to the these questions, spatial information managers (together with spatial

scientists) have to develop a simplified view of the real world, mostly because that real word is

too complex to represent as a computer model.

The complexity can be due to the large number of actors and causal and other interdependencies,

and also to our limitations in understanding the underlying real world processes.

This simplified view on the world results in a model, or sometimes in multiple models.

A model is a simplified representation, description or simulation of reality.

The different models simplify reality by emphasizing some aspects while ignoring others:

1. Information model

An information model emphasizes description of things in terms of attributes and values.

2. Computational model

A computational model, generally, is one that allows to determine certain characteristics by

computation. An example is a route finding model that determines a shortest connection

between a pair of locations.

3. Process model

A process model, as a special case of computational models, emphasizes the dynamics and

interplay between model elements in the domain at hand.

Simulations, for instance, are a specific kind of process model.

4. Visual model

A visual model often emphasizes relationships between elements in the domain of study. Maps,

for instance, are visual models emphasizing spatial relationships.

Models often originate from a specific discipline, such as a water run-off (process) model from

hydrology, and to address more complex problems you often need multiple models from a range of

disciplines.



Learners’ Notes 6

A map is a well-known example of visual model. As a graphic representation, it is a selection

(for instance, roads only) and abstraction (for instance, it uses road classes) from reality. Obviously,

a ‘from A to B’ route finding model is simpler than, for example, a ‘climate change’ process model.

The ‘from A to B’ computational model will make use of a network information model, and then

apply an algorithm that determines a route. The data required are the locations and lengths, and

other characteristics of the roads. A selected routing algorithm takes care of the computations.

At the end, the model has to report and present results of its computations.

When solving spatial problems, maps are the most common, but not the only, tools for

reporting.

In the previous example, a set of driving instructions is another useful report.

In all modeling situations, the objective of the spatial information manager is:

• to support spatial problem solving; and

• to prepare for and support decision-making with those solutions.

Modeling will be dealt in greater depth in Lessons 1.3 and 3.6

Key methodological elements in solving spatial problems

The successful application of models to spatial problems depends on a number of conditions:

• data is needed;

• the process has to be organized;

• the working environment has to be defined;

• functionality to run models and execute spatial analysis operations is needed; and

results, often as maps, need to be disseminated

These elements make up the data and software part of GIS, but in the wider sense, people, and an

organization in which it is used are also part of it.

The organizational factors define the context and rules to capture, process and share geo-

information, as well as the role that GIS plays in the organization as a whole.



Learners’ Notes 7

Data

If you intend to use the collected data, this needs to be made accessible, so that computations can

be made with it. Many data collection methods exist, the choice between them depends on

purpose and application.

Field surveys may measure parcels, prepare for road works; different sensors can be used, such as

those active in meteorological stations.

Once collected, the data needs to be organized in such a way that subsequent use is

easiest.

Depending on data volume, different technologies are available to do so:

• Geographic Information Systems (GISs);

• spatial databases; and

• image servers.

An overarching method of data organization is the data layer: all the data for a single theme is

arranged together.

The route-finding model may be in need of various data layers:

• one for roads and road classification;

• one for street addresses; and

• perhaps another for traffic jam data.

Lessons 1.2, 1.3 and 1.4, will provide more detail on spatial data and information. Lesson 2.7

will give an overview of various aspects of quality of spatial data.

Functionality

Specific functions (or tools) should be available to manipulate the spatial data, or to prepare new

spatial data from different sources to construct solutions to spatial problems.

Elementary functions are those that identify and localize objects.

In the ‘from A to B’ model:



Learners’ Notes 8

• particular roads could be identified by their name (A1), or by their classification (motorway);

while

• start and destination locations could be identified by a street address.

You can also measure things, for instance the length of a road segment, or the density of the road

network in a suburb. In general, these functions are called retrieval, classification, and

measurement functions. In the same example, the routing algorithm itself is a connectivity or

network function.

Most functions combine different data layers (e.g. a land use layer and a layer with a planned road)

to see how much the new road impacts the environment. This can be done in vector or raster

mode, depending on the nature of the available data. Functions like this are called overlay

functions.

Neighborhood functions are applied to a single data layer, and consider the influence of the

surroundings of the object of study. An example is the impact of sound during the day along a road

segment.

Lessons 3.2, 3.3, 3.4 and 3.5 will provide more insight on retrieval, classification, and

measurement functions; overlay, neighbourhood and network functions.



Learners’ Notes 9

Some functions, qualified as interactive visual functions allow for display and visual querying,

following the so-called Shneiderman mantra: “Overview first, zoom and filter, then details-on-

demand.””

Although these functions are apparently part of a graphical interface, they are in fact a

combination of a visual and computational approach. The algorithms take care of often

complex computations and the human visual system detects patterns.

For their spatial data handling, these services commonly use raster and vector representations.

Spatial data handling will be dealt in greater depth in Lesson 2.1

Visualization

It is important that the required data are available on demand, preferably via web services.

Based on the problem at hand they can use the data in their own software environment or can run

spatial analysis operations or compile maps via web services.

Throughout this interactive process GI scientists may need (intermediate) results. These reports

can be sketches, maps, diagrams, tables, text, photographs, and videos, but also notes and

annotated documents collected during fieldwork, metadata from obtained datasets, etc.

Their visualization may provoke thoughts since they show outliers or other spatial patterns that will

make the geoscientists even more curious and might lead to yet another step in the spatial analysis

process. The visual analysis always goes hand in hand with computational operations and is

part of the modeling process.

Lessons 4.3 and 4.4 illustrate in great detail visualization and dissemination of spatial data

The process of spatial information management

The working environment of spatial information management should allow for the connection

between the different phases of problem solving, and the linking of concepts, data, and models.

The process of spatial information management is similar to the process of geographic

information science GIScience (Scientific field that attempts to integrate different disciplines

studying the methods and techniques of handling spatial information.), which contains the following

steps of using spatial data:



Learners’ Notes 10

1. exploration;

2. synthesis;

3. analysis;

4. evaluation; and

5. presentation.

Spatial information management cycle

In theory, having the spatial problem in mind, one starts with the exploration of the available

data, resulting in a research hypothesis or an understanding of the computational approach to take.

As the next step, a synthesis leads to applicable theories or models that will be subject to

analytical functions.

The results will be evaluated and this can be followed by a presentation of the final result,

indicating the confidence one has in the result so one might take appropriate decisions.

In practice, the process may not involve all steps depicted in the diagram, or contain several




iterations.

Standards

GIS technology, spatial data needs to be shared, and in order for this to happen systems need to

be interoperable.

To realize the interoperability standards are essential, i.e., created a network of participants

sharing data, services, application and best practices.

Spatial data infrastructure is a collection of technologies, policies and institutional arrangements

that facilitate the availability of and access to spatial data.

There are series of standards for open interfaces, geo-resources as well as geo-services

published by W3C, ISO and OGC.

Lessons 4.1 and 4.2 will deal in greater depth on international standards, interoperability and

spatial data infrastructure technology.

Spatial information working environment

In recent years, the work environment has developed from typical stand-alone application (as a

desktop) to a networked, often Internet-based environment.

Data and even certain functions are offered as geo-services in a wider Spatial Data Infrastructure

(SDI). A geo-service is a special kind of web service involving spatial data or computations over

spatial data. It is web-based, i.e., it can be used by connecting with it over the web, normally

through a URL.

Geo-services can vary from a simple map display service to one that involves complex spatial

computations . An example of geo-service is GeoNetwork (http://www.fao.org/geonetwork ).

An SDI is a set of institutional, technical and economical arrangement to enhance the availability

(access and use) for correct, up-to-date, fit-for-purpose and integrated geo-information and geo-

services. It makes an attempt to do so in a timely way and at an affordable price to support

decision-making processes. The motto of SDI is ‘collect once – use many times’.

http://www.fao.org/geonetwork�




For reasons that include efficiency and legislation, many organizations work in a cooperative setting

in which geographic information is obtained from, and provided to, partner organizations and the

general public. The sharing of spatial data between the various geographic information systems

(GIS) in those organizations is important and aspects of data dissemination, security, copyright and

pricing require special attention.

Typically, an SDI provides its users with different facilities for finding, viewing, downloading and

processing data. Organizations within an SDI can be widely geographically distributed.

With the development of the internet, therefore, the functional components of GIS have been

gradually become available as web-based applications.

Much of the functionality is provided by those geo-services.

Spatial information systems

The fundamental tool to solve many spatial problems is a Geographic Information System (GIS).

GIS provides a range of capabilities to handle spatial data, including:

1. Data capture and preparation

Typical planning projects require data sources, both spatial and non-spatial, from different

national institutes. The data sources obtained may be from different time periods, and the

spatial data may be in different scales or projections.

2. Data management

With the help of a GIS, the spatial data can be stored in digital form in world coordinates,

allowing proper integration. This makes scale transformations unnecessary, and the conversion

between map projections can be done easily with the software.

3. Data manipulation & analysis

With the spatial data thus prepared, spatial analysis functions of the GIS can be applied to

perform the planning tasks. It is necessary to pay careful attention to the quality level of the

different dataset.

4. Data presentation




What remains is to select the proper media for the data presentation to the intended audience.

For many years, analog data sources were used, processing was done manually, and paper maps

were produced.

The introduction of modern, digital techniques has led to an increased use of computers and

digital information in all aspects of spatial data handling.

The software technology used in this domain is centered around spatial information systems.

Details of architecture and functionality of GIS and spatial databases are discussed in Lessons 1.5

and 2.4..

Spatial data acquisition - primary and secondary data

Spatial data can be obtained from various sources. It can be collected on the ground and in place,

remote from the ground through remote sensing1

Primary data

technology essentially using direct spatial data

acquisition techniques (primary data), or it can be obtained indirectly, by making use of existing

spatial data collected by others (secondary data).

• Generated ground data;

• Terrestrial surveys;

• Field surveys;

• Mobile GIS;

• In situ sensor data; and

• Remotely sensed data.

Secondary data

• Digital data from elsewhere;

• Digital data from agencies, institutes and so on (‘authoritative’ data);

1 Remote sensing - The art, science and technology of observing an object, scene, or phenomenon by

instrument-based techniques. It is ‘remote’, because observation is done at a distance without physical

contact with the object of interest.




• Data sources on the web (both volunteered and authoritative);

• Analog data;

• Digitized paper maps; and

• Scanned maps.

One way to obtain spatial data is by direct observation of the relevant geographic phenomenon.

This can be done:

• through ground-based field surveys; or

• by using sensors in satellites or airplanes.

Many Earth sciences have developed their own survey techniques, with ground-based techniques

remaining the most important source for reliable data in many cases.

With primary data - captured directly from the environment - the core concern in knowing its

properties is to know:

• the process by which it was captured;

• the parameters of the capturing instruments used; and

• the rigor with which quality requirements were observed.

In practice, it is not always feasible to obtain spatial data by direct spatial data capture. Factors of

cost and available time may be a hindrance, or earlier projects may have acquired data that fits the

current project’s purpose to some extent. .

In contrast to direct methods of data capture, spatial data can also be obtained indirectly.

This includes:

• data derived from existing paper maps through digitizing or scanning;

• processed data purchased from data capture firms or international agencies; and so

on.

This type of data - known as secondary data - is derived from existing sources, and has been

collected for other purposes, often unconnected with the current project.




More detail on spatial data acquisition techniques is discussed in Lesson 2.6

Spatial data acquisition – Mobile GIS

With a mobile GIS system and the support of a GPS, we can take GIS to the field on compact

mobile devices.

With such a device, we can see directly where we are in relation to remotely sensed images and

vector data. Data can be recorded directly in a GIS format.

Mobile GIS will be dealt in greater depth in Lesson 2.3.

Spatial data acquisition – Remote sensing

Satellites have allowed us to realize geocentric reference systems, and to increase the level of

spatial accuracy substantially. They also play a key role in mapping, surveying, and in a growing

number of applications requiring positioning techniques.

For fieldwork that includes spatial data acquisition, the use of satellite-based positioning is

considered indispensable.

With remote sensing (RS) is meant the observation of the Earth’s surface with satellite images

and aerial photographs.

The electromagnetic (EM) radiation energy from the sun, after interaction with atmosphere and the

Earth surface is detected and registered by a sensor, and stored as image data.

From these images, spatial information can be extracted.

Remote sensing will be dealt in greater depth in Lesson 1.6.

Spatial data acquisition – RS techniques and products

The general mapping method with remote sensed data consists of the interpretation of images

for sampling design, field data collection and analysis.




The assumption in mapping with the help of remote sensing images is that areas that look

homogeneous in the image have similar features on the ground. Maps and inventories should

reflect what is actually on the ground. Therefore, field visits should be made to observe what is

there in reality.

For more detail on remote sensing techniques and products, refer to Lesson 3.1

Spatial data acquisition – integration of different sources

In geo-information science and Earth observation (The process of gathering information about

physical, chemical, biological, and geometrical properties of the planet Earth), spatial data is often

analyzed in a context that is application-dependent, and is aimed at extracting new information that

is relevant for certain applications.

In such cases, we may need to integrate data from different sources to improve the retrieval

of surface properties, or to detect changes that would otherwise remain undetected.

Therefore, a GIS project usually involves multiple data sets, and the issue of how these multiple

sets relate to each other is an important step.

Lesson 3.7 will provide greater detail on the integration of various data sources.

Spatial analysis – Data integration

Before carrying out analysis of any kind, attention should be given to differences in spatial and

temporal reference systems and resolution.

At this purpose, as a GIS user, you need to understand basic spatial referencing concepts, like

reference surfaces, coordinate systems, and map projections.

Spatial referencing and map projections are discussed in detail in Lesson 2.5 and Lesson 2.6.




Spatial analysis – Modeling

Modeling is a term that is used in many different contexts, and hence it can be difficult to

understand what one means when talking about models. In this module, we generally understand

modeling as a schematization of reality with operational potential.

More specifically, spatial modeling refers to a set of analytical procedures used to derive

information about spatial relationships between spatial phenomena.

Lesson 3.6 will provide an overview and discusses important applications of spatial data

modeling, as used in the field of Remote Sensing and GIS.

Spatial analysis– Error propagation

Even if you acquire data with the best possible quality, you cannot guarantee that the results of

further, complex processing can maintain that quality.

As the number of processing steps increases, it becomes difficult to predict the behavior of error

propagation.

Errors may affect the outcome of spatial data manipulations. In addition, further errors may be

introduced during the various processing steps.

Common sources of error introduced into GIS analyses and propagation of these errors is a topic

discussed in Lesson 3.8. .

Spatial data reporting

The final step in spatial management projects is reporting.

Maps are tools for reporting results of spatial analyses. Traditionally, maps are divided into:

• topographic maps, which visualize, limited by their scale, the Earth’s surface as accurately

as possible ; and

• thematic maps, which represent the spatial distribution of particular themes (socio-

economic and physical themes).




Lesson 4.4 explains the relation between GIS and maps, discusses the main types of visualization

used for reporting with spatial data, and introduces the basic concepts of map design.

The cartographic visualization process consists in the translation or conversion of spatial data

from a dataset into graphics. These are predominantly map-like products.

During the visualization process, cartographic methods and techniques are applied.

There is a difference in the visualization strategy for public visual communication and for private

thinking or exploration.

An appropriate symbology has to be chosen in relation to the nature of the data and measurement

scales.

Summary

Spatial data is ubiquitous. Spatial information systems and applications are tools used on a daily

basis by many of us:

• lay users (e.g., in search of the best place to have dinner);

• GIS experts (e.g., computing the area and volume of excess soil that needs to be removed

for a new road construction);

• spatial information managers (e.g., for reviewing the catalog of map products organization

is selling); or

• decision makers (e.g., devising a new urban plan for a locality with high population density).

To use spatial information systems for interpretation of spatial data and results of spatial analysis,

the knowledge of fundamentals of spatial information science is needed.

This module provides fundamentals of spatial data, spatial information systems and the operational

use of spatial data in the context of spatial data infrastructure.

This module also introduces different models and spatial data types that are used to represent real

world phenomena using information and communications technologies.

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