Output formatting

GIS output is in the form of:

1. Maps
2. Charts

1. Charts can be used to display tabular data
2. Types of charts available are generally of six types. They are:
1. Area chart
2. Bar chart
3. Line chart
4. Column chart
5. Pie chart and
6. Scatter chart

A map is a spatial model of the real world and is differentiated from it (the real world) due to:

• Abstraction 
• Imaginary & physical features
• Past, Present and future features
• Focus
• Selection and classification of features to be included in map
• Simplification
• Simplification of complex features 
• Exaggeration of small features
• Symbolization
• Using symbols to represent objects
• Scale
• Ratio of distance on map to distance on Earth
• Projection
• Representing curved Earth on a flat plane
• Purpose 
• To describe, measure, communicate/persuade.

Types of maps 

1. Planimetric
2. Topographic
3. Cadastral
4. Image
5. Thematic

Below listed are a few general principles to create maps:

1. Output maps (GIS output) should be kept simple to understand and not cluttered with too much information by relying excessively on software defaults
2. Only the area under study should be included along with a small map inserted showing the relative location.
3. The maps should be created keeping in mind the final publication sale. This implies a size reduction for report or journal publication. It should be borne in mind that point symbols reduce in visibility at twice the rate of line features.
4. The title should be mentioned at the top in thick line font: serif/sans-serif; case not critical
5. Map labels should be formatted as per the following guidelines:
• labels should be in serif font (first letter uppercase and remaining lowercase)
• labels should be placed above and to the right of the feature (2nd choice: above and to the left)
• Font size hierarchy should indicate relative importance
• Water features traditionally labeled in italic font
6. The map should be created in black and white and the colour should be added later

Colour

Hue refers to the actual colour; Saturation refers to the amount of the colour, while value refers to the amount of black used.

For optimum readability, low saturation in the background and high saturation in the foreground is recommended.

Choice of shading

In case of:

• Quantitative data:
• Symbols should have visual progression corresponding to data values
• For polygons, using monochromatic colour ramp: same colour, different saturation
• can highlight top and bottom with contrasting colour
• "visual progression" should be ensured if different fill patterns are used
• For point symbols, different sizes of the same symbol must be used.
• Qualitative data:
• For polygons, different fill colours or different patterns should be used
• For point symbols, same size of different symbols should be used
• The legend should be big enough to clearly show different fill patterns

Simple analysis in GIS

Simple analysis in GIS:

1. Spatial measurements: GIS makes spatial measurements easy to perform. Spatial measurements can be
1. Distance between two points
2. The area of a polygon
3. The length of a line or boundary.
4. Calculations can be 
1. of a simple nature, such as measuring areas on one map, or 
2. complex, such as measuring overlapping areas on two or more maps. 
2. Information Retrieval: A GIS can help point at a location, object, or area on the screen and retrieve recorded information about it from the Database Management System (DBMS) which holds the information abut the map’s features. In order for a GIS to answer the question "what is where?" we need to carry out retrieval. Retrieval is the ability of the DBMS or GIS to get back on demand data that were previously stored (Clarke, 1997). As Clarke put it "Geographic search is the secret to GIS data retrieval" so GIS systems have embedded DBMSs, or link to a commercial DBMS.
3. Searches by attribute: Most GIS systems include a basic relational database system. All DBMS include functions for basic data display. Searches by attribute are then controlled by the capabilities of database manager. Find is the basic attribute search. Find is intended to get a single record.
4. Searches by geography: In a map database the records are features. The GIS spatial retrieval is the generating maps, which allow searching for information visually and highlight the result. For example to generate a report; the spatial equivalent would to produce a finished map, the spatial equivalent of a find is locate. Spatial equivalents of the DBMS queries result in locating sets of features, or building new GIS layers. These include: Spatial searching, browsing the map and picking features, Spatial sorting to identify features that result from attribute sorting.Combinations of spatial and attribute queries can build some complex and powerful GIS operations. Typical GIS searches are point in polygon, line in polygon, and point distance to line.
5. Spatial overlay: One basic way to create or identify spatial relationships is through the process of spatial overlay. Spatial overlay is accomplished by joining and viewing together separate data sets that share all or part of the same area. The result of this combination is a new data set that identifies the spatial relationships.
6. Boundary analysis: Boundary analysis, which is referred to as districting and helps define regions according to certain criteria. This procedure is used to define area of specific demographic characteristic.  Since districting is normally an iterative process involving the development of numerous scenarios based on various combinations of desired criteria, the computing power of the GIS helps in saving time and effort. GIS helps to interactively define proposed boundaries and have related population totals automatically computed in rapidly and efficiently. 
7. Buffer analysis: Buffer analysis is used to identify areas surrounding geographic features. The process involves generating a buffer around existing geographic features and then identifying or selecting features based on whether they fall inside or outside the boundary of the buffer.
8. Neighborhood Operations: Neighborhood operations can evaluate the characteristics of the area surrounding a specific location. Neighborhood operations include the following: Search (Average, Diversity, Majority, Maximum/Minimum, and total), Topographic, Interpolation (interpolation involves using known cell values to predict predicting the values of intermediate cells), and Contour Generation. 
9. Connectivity Functions: Connectivity functions involve traversing an area and accumulating values: Contiguity measures, Proximity, Network functions, Spread, Seek and Stream functions

Modelling in GIS

A model is a representation of some part of the real world and hence has certain characteristics common with the real world. It therefore possible to study and operate on the model instead of the real world under various conditions. This is a very effective method to answer 'what if' questions. The procedure can be repeated by changing the data or altering the parameters of the model.
A map is a miniature representation of some part of the real world is a model while databases are also models. Maps and databases are usually static models.
Modelling and GIS are inseperable as GIS is a tool for modelling the real world.
Models can be static if the input and output both correspond to the same point in time, or dynamic, if the output represents a later point in time than the input. Static models often take the form of indicators combining various inputs to create a useful output. On the other hand, dynamic models represent a process that modifies or transforms some aspect of the Earth's surface through time.
In the context of GIS, modeling is defined as the operations of GIS that emulate processes of the real world at an instant or over a period of time. GIS models can be used to evaluate or predict future landscapes.

Analog GIS model is a representation of the real-world system in which every part of the real world appears in miniature in the model. In an analog model, all aspects of the system must be scaled by
the same ratio for the model to be valid.
In a digital model, all operations are conducted using a computer. Data is assembled in a data model and relevant aspects of the real world and coded to patterns of 0s and 1s. Digital models do not have a representative fraction. The level of geographic detail is captured in the spatial resolution.

Discrete models imitate processes that operate between discrete entities. Continuous models are formed on variables that are continuous functions of location. The discrete-object view and the continuous field-view are widely accepted distinctions between the conceptualizations of geographic space and geographic variation.
Geographic space is empty except where it is occupied by point, line or area objects that may overlap, do not exhaust all available space and are countable. For example, the discrete-object view is best to describe and represent biological organisms or man-made features like buildings, vehicles or fire hydrants.
In the continuous field view, there are no gaps in coverage and there is exactly one variable at each location.Continuous field models express knowledge of the operation of the physical system in terms of partial differential equations which relate the values, rate of change through time, spatial gradients and spatial curvatures of the continuously varying quantities.

Individual and aggregate models- It is possible to model any system using a set of rules about the mechanical behavior of the system’s basic objects (individual model). However, if the number of basic objects is far too large for this approach to be practical, the problem is solved by replacing individual objects with continuously varying estimates of abstracted properties such as density. Another approach is to aggregate (aggregate model) individual objects into larger wholes and to model the system through the
behavior of these aggregates.

Cellular Automata: In a cellular automaton, spatial variation is represented as a raster of fixed resolution, each cell is assigned to one of a number of defined states. Such models have been used widely to study processes of urban growth, in which case the possible states are limited. At each time step, the next state of each cell is determined by a number of rules based on the properties of the cell and its neighbors and on the states of the cell and its neighbors. The concepts of cellular automata were first explored by John Conway.

AGENT-BASED modeling is a computer based simulation in which a program is written to simulate the real-world situation. An agent-based model consists of an environment or framework that defines the scope and rules of actions, along with a number of agents representing one or more actors whose parameters and behaviors are defined. When the model is run, the characteristics of each agent are tracked through time and space. Agent-based modeling has found many interesting applications to geographic phenomena. Several efforts have been made to apply agent-based modeling to the emergence of land-use and land-cover patterns, with particular emphasis on the processes that lead to
greater fragmentation of land cover as a result of development and thus to problems for species that require specialized natural habitat.

Maintenance and analysis of spatial and attribute data

Maintenance and analysis of spatial data
Maintenance and analysis of spatial data can be performed by one of more of the operations described below:

1. Format transformation - These functions convert between data formats of different systems. For example: reading a DXF file into a GIS
2. Geometric transformation - These functions help transform device coordinates (coordinates from digitizing tables or screen coordinates) into world coordinates (geographic coordinates, etc)
3. Map projections - These functions help map spatial geographic coordinates on a flat surface (map) and vice-versa
4. Edge matching - This is the process of joining two or more map sheets. Feature representations should be matched at map sheet boundaries.
5. Editing of graphic elements - These functions are used to remove and correct errors in order to prepare a clean dataset for topology building
6. Coordinate thinning - This function involves removal of redundant coordinates from line representations. 

Maintenance and analysis of attribute data

The functions involving maintenance and analysis of attribute data correspond to functions of conventional database systems and contain attribute editing and attribute query functions.

Text patterns and line styles

Text Patterns


Line styles
Line Styles define how line features are rendered on a map. Line styles offer numerous controls for color, gradient or pattern fills, dash patterns and many other properties.

Line styles have the following properties listed below:

1. Name - Each line style is given a name for easy identification and use when creating a map
2. Colour - This property controls the colour and appearance of the line feature. The line can be filled with a colour, gradient, hatch pattern or bit mapped texture.
3. Thickness - This property sets the thickness of the line (in pixels).

Pattern Type - Selects whether to use a basic (predefined) dash pattern or create a custom pattern. 

Dash Style - Allows a choice of dash style to be used. The following styles are generally used:

1. Solid: This pattern draws a solid outline without dashing and this is the default setting
2. DashDot: This pattern draws a dashed outline with a “dash-dot” pattern.
3. DashDotDot: This pattern draws a dashed outline with a “dash-dot-dot” pattern
4. Dot: This pattern draws a finely dotted outline
5. Dash: This pattern draws a dashed outline

A Custom Pattern Type is also available where the Dash Style property will be displayed as a text box where a custom dash pattern is entered. Dash patterns are expressed as a comma-delimited list of pixel lengths. The first number represents the length of the first dash in the pattern. The next number represents the length of the first space after that dash, and so on. When the end of the sequence is reached, it repeats.

A Line Join determines what type of joint to draw at the point where one line ends and another begins.

Sampling GIS data

Sampling GIS data
Since it is virtually impossible to obtain detailed information regarding every location on the Earth's surface (specifically for GIS), it is best to take samples from a smaller representative subset. GIS deals with explicitly spatial data. The two ways in which spatial data can be sampled is:
-Directed sampling and
-Non-directed sampling
Directed sampling involves making decisions about the objects to be viewed and cataloged. This population occupies an area and both the area and the sample population form the sampling frame.
Non-directed, probability-based sample is preferable since it eliminates bias.
Probabilistic sampling is of four types:
-Random sampling
-Systematic sampling
-Stratified sampling and
-Homogeneous sampling
The above sampling types can be combined to create a hybrid sampling scheme.
Random spatial sampling is the most basic sampling design. It allows each point, line, area or surface feature to be selected with the same probability as the next.
Systematic sampling use a repeatable pattern as a basis for selection.
Stratified spatial sampling selects small areas within which individual spots, objects or features are sampled. Stratifying simplifies the process by dividing the task into small regions.

Data quality refers to the fitness for use of data for intended applications. The components of data quality are listed below:
-The data must be reliable and accurate to be considered as usable. It should be in agreement with the real world being represented.
-The data must be current and up-to-date for the intended application.
-The data must be relevant in terms of content and level of detail
-The data must be timely for the application
-The data must be complete and precise or the degree of uncertainty must be indicated
-The data must be concise and intelligible (comprehensible to the user)
-The data must be stored in a format that can be conveniently handled (data handling involves one or more of the following operations: maintenance, transmission, distribution, classification, re-sampling, retrieval and updation)
-The data must be documented regarding its source to enable users to determine their suitability for a certain application
-The data must be stored in map projection that best meets the requirements of the application with regards to the preservation of area, shape, distance and direction
-The data should be captured at a scale using a classification scheme suitable to the application for which it will be used
-Cartographic properties (physical condition of mapping media, quality of line work, use of color and symbology, classification of features, map maintenance and revision cycle) play an important role in determining the quality of data. 

Types of errors in GIS data, their elimination and accuracies

TYPES OF ERRORS IN GIS DATA THEIR ELIMINATION AND ACCURACIES 
Maps created by GIS have deficiencies.  These deficiencies occur due to “Errors” that may have taken place at different stages of GIS implementation. Flaws in data are referred to as errors. An error is the physical difference between the real world and the GIS output. Errors may be single, definable departures from reality, or maybe persistent widespread deviations throughout a whole database. Errors reduce the accuracy of the map generated. Using well defined and controlled procedures errors can be avoided.
There are two types of errors in GIS:
        I.      Source Errors:
                 They are the errors that are present in “Source Data” that is given to the GIS. They occur      before the actual implementation of GIS:

                                            a.      Instrumental inaccuracies:
                                               ● Satellite/ air photo/ GPS/ surveying (spatial).
                                               ● Inaccuracies in attribute measuring instruments.
                                            b.      Human Processing:
                                               ● Misinterpretation (e.g. photos), spatial and attribute.
                                               ● Effects of scale change and generalization.
                                               ● Effects of classification (nominal / ordinal / interval).
                                            c.      Actual Changes:
                                               ● Gradual 'natural' changes: river courses, glacier recession.
                                               ● Catastrophic change: fires, floods, landslides.
                                               ● Seasonal and daily changes: lake/sea/ river levels.
                                               ● Man-made: urban development, new roads.
                                               ● Attribute change: forest growth (height etc.), discontinued trail /roads,                 road surfacing.


        II.      Processing Errors:
                  They are the errors that occur during the processing of the data i.e. during the                       implementation of GIS.

                   a.     Input:
                       ● Digitizing: human error, the width of a line, spikes, knots, also entering attribute                                                      data.
                       ● Dangling nodes (connected to only one arc): permissible in arc themes (river                    headwaters etc.).
                       ● Pseudo-nodes (connected to one or two arcs) - permissible in island arcs, and where    attributes change,
                            e.g. road becomes paved from dirt or vice versa.
                       ● Projection input error.

        b.     Manipulation:
                ● Interpolation of point data into lines and surfaces.
                ● Overlay of layers, digitized separately, e.g. soils and vegetation.
                ● The compounding effects of processing and analysis of multiple. layers: for example, if        two layers each have correctness of 90%, the accuracy of the resulting overlay is                                   around 81%
                ● Density of observations.
                ● Inappropriate or inadequate inputs for models.

         c.     Output:
                 ● Scale changes - detail and scale bars.
                 ● Color palettes: intended colors don't match from screen to Printer.

In addition to the above, errors may also be introduced during data transfer and conversion. These types of errors, may lead to loss of data.
It is a misconception that data from a GIS is of a higher quality. The quality of information from a GIS depends upon the quality  of the data used as input to the GIS.

Precision indicates how closely several positions fall in relation to each other. Precision is the recorded level of detail of the data.

Accuracy is a measure of the closeness of one or more positions to a position that is known and defined in terms of an absolute reference system. Accuracy is the extent to which an estimated data value approaches its true value (Aronoff, 1989).

Bias in GIS data is the systematic variation of data from reality. Bias is a consistent error throughout a dataset. Bias in GIS data may be caused due to human or technical sources.

ERROR, PRECISION, ACCURACY and BIAS affect the quality of individual datasets.

GIS error elimination
The simplest method of checking for data errors in GIS is by visual inspection. Comparison of data in GIS format with the original map reveals major errors. Double digitising is an error checking method used by large companies. This involves digitising the same map twice and comparing the two copies to identify inconsistencies. This is a costly and time consuming method of error checking. Statistical methods can be used to pinpoint potential errors.