eChapters

Scale insect: Lepidosaphes sp.: Diaspididae

The mature scale is elongated and oval in shape and dark grey in colour. They infest the stems, leaves and petioles, causing shriveled leaves due to sucking of the sap. The affected parts wither and dry up. In severe infestation leaves dry up and give sticky look and gradually the vine wilts.

Cardamom thrips: Sciothrips cardamomi: Thripidae

Thysanoptera

This thrips attacks other crops like amomum,  tea, colocasia, grapevine, castor, cotton, ginger and turmeric also. The thrips are minute greyish brown insects with fringed wings. They breed on unopened leaves, spindles, leaf sheath, flower bracts and floral tubes .

Cinnamon butterfly: Chilasa clytia clytia : Papilionidae

Larvae defoliate plants. The larvae are pests on many crop plants and weeds and are common in the plains. The larvae are jet black at hatching. They develop a series of broad white patches. The adult is a common butterfly rich velvety brown, and spotted with white dots. The hind wings have arrow shaped streaks and crescent shaped spots.

Stem-borer: Sahyadrassus malabaricus: Hepialidae

Presence of the pest is understood when chewed tissue is seen at at the col­lar region. The mouth of the tunnel in which the borer feeds show pieces of bark, wood and frass particles held together by silk. The saplings break off at the point of injury. When the main stem is attacked, there is mat-like frass at basal region of stem. Larvae girdle main stem and bores downward killing the tree.

Adult moth is peculiar and hangs vertically down by the support of two pairs of legs. Egg period is for 7-10 days, and larvae live for about 10 months inside the larval tunnels. They pupate for 3-5 weeks.

Coriander aphid: Hyadaphis coriandri: Aphididae

Coriander aphids are yellow-green in color, dusted with grayish wax. They have short, dusky, slightly swollen, siphunculi (or cornicles). The Aphids form dense, damaging colonies on leaves, heads, and stems of their host plants.

In its native land, Florida, USA, the life cycle of the coriander aphid is similar to that of other host-alternating aphids. Overwintering eggs occur on other hosts.  A fundatrix (or stem mother) hatches from the egg in the spring. Her offspring are parthenogenetic, and the winged-females are spring migrants. They colonize summer host plants in the family Umbelliferae.

Shoot borer: Conogethes punctiferalis: Pyraustidae

The main damage done by the caterpillar of this pest is to bore holes in the pseudostem and to plug it with excreta, causing dead hearts. Larva is pale greenish with pinkish tinge and dark head and has fine hairs on the body with a pro thoracic shield. Adult is a medium sized moth, pale yellowish with small black spots on the fore and hind wings.

Management involves spraying dimethoate or malathion 1.5 ml/lit or phosphamidon 0.75 ml/lit of water. This is mainly intended to manage the adults flying in the crop canopy and the first instar caterpillars that are seen on the outside of the plants and before thsy enter the pseudostem. The spray has to be given at the very first spotting of dead haearts.

Grasshoppers: Mecopoda elongata :Tettigonidae; Oxya velox: Acrididae

These pests occur both in nurseries and plantations. Both adults and nymphs feed on leaves by scrapping and eating outer tissues and margins. The emerging leaves are nibbled and the spot holes appear when the leaf unfurls. In severe conditions the entire leaf is eaten leaving behind only the midrib.

Scale insects:Pseudaulacaspis cockerelli, Parasaisettia nigra:Coccidae

Attack Adults and nymphs feed on plant sap and cause wilting and drying of seedlings and young plants.
 
Brown soft scale: Lecanium sp. (=Coccus) hesperidum : Coccidae

Thrips: Thrips tabaci: Thripidae

The nymphs and adults of the thrips desap from the narrow spaces between the inner leaf axils. Leaves are faded, with curled tips and shining silver streaks or brown spots.  Many minute yellow or white insects are seen moving, generally near the base or middle of leaves and they suck the sap from the plants.  Plants are faded and the yield is reduced.

Pollu beetle: Longitarsus nigripennis:Chrysomelidae

This pest is monophagous to pepper and no alternate host has been reported so far.

Nature of Damage

Adult is a shining blue flea beetle with yellow head and thorax and has thick enlarged femur and tibia. The adult beetle is only the size of a pin head.  The infested berries dry up and turn dark in colour. Berries become hollow and crumble when pressed and such hollow berries are called “Pollu” meaning empty. The eggs are laid on the berries @ 1-2 eggs in each hole. Eggs hatch in 5-8 days. The grub period extends for 30-32 days. The emerging grubs feed on the berries of pepper and consume the internal contents.

Aphids: Aphis gossypii :Aphididae

The aphids congregate on growing points and suck the sap. Honey dew secreted by the aphids can cause sooty mould in severe infestation. Attack leads to yellowish speckling and curling of leaves.
 
Thrips: Caliothrips indicus: Thripidae

Characteristic silvery white specks with black dots which are mostly faecal pellets, develop on leaves. Consequently, the leaves dry up.

Cassava scale : Aonidomytilus albus: Diaspididae

White elongated scales appear in colonies on stems. Plants die in the field and under storage.  Stem becomes weak and dry, causing side branching to give bushy appearance.  Viability of planting material is reduced, resulting in poor establishment.

Leaf-and shoot folders: Hymenia recurvalis : Pyraustidae

Pycnarmon cribrata;Phostria piasusalis

Aphids: Myzus persicae, Lipaphis erysimi and Brevicoryne brassicae Aphididae
 
Aphids suck the sap from potato foliage, causing wrinkling, cupping and downward curling of young leaves. The older leaves will become yellow, wilt and fall off.  They also excrete honeydew on which sooty mould develops covering affected parts with a thin superficial black coating that hinders photosynthetic activity of leaves resulting in stunted growth of plants. In addition, they also act as vectors, for transmitting potato leaf roll virus. Major damage by M.persicae and A. gossypii is through transmission of potato leaf roll virus.

Sweet potato weevil: Cylas formicarius: Apionidae

It is a specific pest of sweet potato and the most important one. Adult weevils are ant-like, slender bodied having elongated snout-like bluish-brown head with geniculate antenna, bright red thorax and leg tips and bluish-brown elytra. Females make small cavities on the tubers or stems and lay eggs singly. Each female lays 180-200 eggs. Grubs are elongate, legless, pale-yellowish white in colour with smaller brown head. Pupation takes place in larval tunnels in the tuber. Incubation, grub and pupal stages last for 5 to 8, 14 to 17 and 4 to 5 days respectively. Life cycle is completed in 4-5 weeks and adults live for 95-110 days. The grubs bore into stems, tunnel inside and feed on soft tissues. Grubs and adults bore into tubers both in field and storage godowns. Affected tubers develop dark patches, which later start rotting. Feeding holes and adult emergence holes can be seen on vines and tubers.  In severe infestation the collar region of the plants shows unusual thickening due to proliferation and hypertrophy of tissues causing a characteristic terpenoid odour.  Even the slightly damaged tubers are unsuitable for cooking and consumption.  Hence yield loss in practice may come to 100% in many cases.

Amaranthus stem weevil: Hypolixus truncatulus :Curculionidae
 
It is an important pest of amaranthus and is widely distributed in India. It attacks both wild and cultivated amaranthus, especially the leafy varieties with large leaves. Hence, management involves destruction of unwanted, weedy wild amaranthus plants, to reduce the breeding place for the weevil. Adults are ash-grey in colour, with elbowed antennae and brown elytra. Females lay eggs singly in holes made on the stem, branches, petioles or midribs. Eggs are smooth, oval and pale yellow in colour. Incubation period is 3 to 8 days.

Shoot and fruit borer: Earias vitella (=E.fabia), E. insulana: Noctuidae

This is a pest on cotton, bhendi, hibiscus, holly hock and other malvaceous vegetables. The damage is due to the larva that bores into tender terminal shoots in the vegetative stage and flower buds;  flowers and young fruits in the fruit formation stage.  The damaged shoot drops, wither and dry up.  The infested fruits have bore holes, are deformed and become unfit for consumption.  Bore holes are plugged with excreta.

Shoot and fruit borer: Leucinodes orbonalis: Pyraustidae

The pest is active throughout year, but more during monsoon period. Heavy losses up to 60% can occur.

The adult is a greyish brown medium sized moth with white and hyaline wings, having triangular brown and red markings on forewing. A crescent shaped mark facing outside at the apical margin is typical for the species. About 150-250 creamy white eggs are laid singly on leaves, tender shoots, flowers and developing fruits. Egg period is 3-5 days and the hatched larva completes its life in 15 days with 5 instars.

Diamond back moth: Plutella xylostella: Plutellidae
 
The adults of this moth are small, greyish brown having pale whitish narrow wings and with inner margins yellow. There are three pale whitish triangular markings on hind margins of each forewing. At rest, when both forewings come together, they form a dorsal median patch of three diamond shaped yellowish white spots that give them their name diamond back moth. Hind wings have a fringe of long hairs.

Chilli thrips: Scirtothrips dorsalis, Thrips palmi: Thripidae

The adults of this insect are small brownish black with fringed wings. The nymphs are slender and yellowish straw coloured, later acquiring more brownish hue. They lacerate the tissues and feed on the exuding sap. Leaves become crinkled, curled upward and shed. The attack can be confused with chilli mosaic symptom.  In capsicum and chilli, they also feed beneath calyx of flowers and fruits causing flower/fruit drop and reduction in yield and market value of capsicum. Buds become brittle and drop down. Plants get stunted and bronzed. The fruits if at all formed are crinkled and small.

The cowpea aphid: Aphis craccivora: Aphididae

Hosts
 
Groundnut, red gram, peas, beans, safflower, lab-lab niger

Symptoms of damage
 
Both nymphs and adults cause the damage by sucking the plant sap from shoot tips, pods and leaves.  Infested pods become malformed and withered.  Severe infestation may result in complete drying of affected pods.  They also act as vector of virus diseases in pea. 

Fruit flies: Bactrocera cucurbitae:Tephritidae

This pest is more serious in bitter gourd, snake gourd and to a certain extent on ridge gourd. Other cucurbits are attacked to a lesser level. Damage by this pest causes losing the fruit quality, rotting and fruit drop. The adults lay many eggs inside the fruit sing their big ovipositor that hatch out into worm like apodous maggots. Feeding by the maggots on the pulp of fruits result in rotting, yellowing and fruit drop. The dropped fruits allow pupation by the maggots in the soil from where they emerge out as adults and start egg laying on fresh frits.

Sciarid(mushroom flies): Bradysia paupera, Lycoriella auripila: Sciaridae

The larvae feed on the mycelial attachments and restrict the growth of the spawn. The maggots also feed on the developing mushrooms or pinheads and thus cause loss in yield. They also bore the developing mushrooms and render them hollow.

Fruit borer: Helicoverpa armigera: Noctuidae

This is a medium sized moth yellowish brown in colour with a wing

span of about 3 cm and a body length up to 1.4 cm. There is a dark speck and a dark area on the outer margin of each forewing. The forewings also have greyish wavy lines and black spots of different sizes on the upper side.

Reducing insecticide application to the lowest level is the motto of IPM in vegetables. Monitoring for the initial build up and timely mechanical removal of the first population is a must in vegetables. Always remove the first affected plants, by diseases like mosaic, and destroy them by burying or burning. Remove insect eggs, insect affected plant parts and adults and immature stages of insects, and destroy them every day. Do not throw them in the field. They will either come back, or pupate and attack later. During nursery stage, use wood ash, cow dung solution, and plant extracts to deter all pests. Use only low residual insecticides like Dichlorvos 1ml/l, Malathion 2ml/l, Endosulfan 2ml/l or Quinalphos 2ml/l, in spots, at the beginning of attack itself.

1.1 Definition of Statistics

Statistics has been defined differently by different ‘Statisticians’ from time to time. These definitions emphasize precisely the meaning, scope and limitations of the subject.  The reasons for such a variety definitions may be stated as follows:

    i.    The field of utility of statistics has been increasing steadily and
    ii.    The word statistics has been used to give different meanings in singular (the science of statistical methods) and plural (numerical set of data) senses.

In order to have a clear grasp of the subject, we give below some selected definitions of statistics which are grouped under two main heads: ‘Statistics as numerical data’ and ‘Statistics as Methods’.  The former one is in plural sense and the latter singular sense.

2.1 Quantitative Classification

It the data are classified on the basis of phenomenon which is capable of quantitative measurement like age, height, weight, prices, production, income expenditure, sales, profits, etc., it is termed as quantitative classification. The quantitative phenomenon under study is known as variable.  For example the daily incomes of different retail shops in a town may be classified as under.

3.1 Introduction

One of the most important objectives of statistical analysis is to summaries the information contained in the data which may be considered either as a population or as a sample (or sub-population). If the set of data consists of all conceivably possible (or hypothetically possible) observations of a certain phenomenon or a variable, we call it as population. If a set of data contains only a part of these observations, that is selected values of a variable, we call it as sample. Frequency distribution and graphical representation summarize to some extent.  They reduce a mass of data to a more understandable form but not to a single value. The aim of this lesson is to determine descriptive measures or statistical constants which will describe the nature of the data.

4.1 Introduction and Measuring

A measure of central tendency exhibits only one of the important characteristics of distribution, namely they typical representative figure to the whole set of its values. This measure alone will not adequately describe a set of observation unless all the observations are alike. Moreover one cannot understand completely about the data set with a measure of central tendency.  Because in real practical situation there may exits a number of sets of observations (or different distributions) whose measures of central tendency are same but they may differ individually from each other in a number of ways.  In particular two sets of values have the same mean but whilst one set of data is grouped close to the mean the other one is more spread out.  The following examples will illustrate this view point clearly.

In day-to-day life, one comes across many statements which create doubt about certain happenings and one may want to determine the chance of occurrence of a particular happening, e.g., if one desires to know the chance of rain based on some metrological observations.  In the case of coin tossing, one wants to know what will be the chance of winning if a person always stakes for head in a fixed number of tossing. There can be innumerable such queries which one would like to make and meanwhile evaluate mathematically the chance of the occurrence of such possibilities.  This invoked the need of probability theory. The theory of probability came in existence when problems of games were referred to mathematicians like Karl Pearson, Laplace and others. Complicated applications involving advanced theory of probability and axionmatic approach of probability are not dealt within this book.

It has been a general notion that if an experiment is conducted under identical conditions, values so obtained would be similar. But the experiences of people have dispelled this belief. Observations are always taken about a factor or character under study, which can take different values, and the factor or character is termed as variable.
Hence, we have a set of outcomes (sample points) of a random experiment. A rule that assigns a real number to each outcome (sample points) is called random variable. The rule is nothing about a function of the variable, say, X, that assigns a unique value to each sample point of the sample space.

7.1 Meaning of Sampling
    
Before we consider sampling theory, we first define the terms population and sample.
 
The word ‘population’ or ‘universe’ in statistics is used to refer to any collection of individuals or of their attributes or of results of operations which can be numerically specified. For example the assemblage of heights of all the individuals in the population is called a population of heights.  Similarly we may consider the population of weights, wages, yields of grain; prices of wheat, mileages of automobile tyres, etc. a population containing a finite number of individuals or members is called a finite population.  For instance the population f ages of the workers in a factory constitute a finite population of ages. A population with infinite number of members is known as infinite population.  For example the population of pressures of temperatures at various points in the atmosphere is an infinite population. 

8.1 Statistical Hypotheses

in attempting to reach decisions about the populations, it is useful to make assumptions or guesses about the populations involved. When the assumption or statement about a phenomenon occurring under certain conditions is formulated as proposition it is called a scientific hypotesis. We can construct criteria by which a scientific hypothesis may be ‘the  yield of a new paddy variety will be 3500 kg per hectare’. In statistical language it may be stated as ‘the random variable (yield of paddy) is distributed normally with mean 3500 kg/ha’. Such a statement or proposition about the probability distribution of a random variable is called the statistical hypothesis.

We have seen the procedures of testing hypothesis about population parameters in chapter 11. Frequently our interest is to test whether a set of observed values are in agreement with those which would occur if some specified hypothesis were true. The X² statistic provides a measure of agreement between such observed and expected frequencies.

The X² distribution has a number of applications in testing of hypotheses. It is used to test, (1) the independence of attributes, (2) the  goodness of fit, (3) the homogeneity of variances, (4) the homogeneity of correlation coefficients, and (5) the linkage in genetic problems. For applying X² test procedure we have to use only the actual observed frequencies and not the percentages or ratios. Further, the samples should be independent and the observations within a sample should be non-overlapping. The expected frequency in each category should be more than 5. The number of observations should be large, say, more than 50.

10.1 Introduction

In previous lessons we have been dealing with problems connected with variation occurring in any one variable, say the income of a person, the height or weight of an individual, the wage of a worker in a factory, the sales of a shop, demand of a commodity etc.  The values of a single variable were obtained from every member of the population and the different characteristics were described by certain descriptive constants after grouping into frequency distribution.  In this lesson, we shall be dealing with problems wherein there are paired variables and every individual or member of the population exhibits two values, one for each of the variables under consideration.  Example of the two variables which are to be studies together are (i) the income and expenditure of a household, (ii) the price and demand or supply of a commodity, (iii) the height and weight of a student, and (iv) the grain yield and straw yield of paddy, (v) Height of father and height of son, etc.

11.1 Introduction

The analysis of variance is a powerful statistical tool for tests of significance.  The test of significance based on t-distribution is an adequate procedure only for testing the significance of the difference between two sample means. In a situation when we have three or more samples to consider at a time an alternative procedure is needed for testing the hypothesis that all the samples are drawn from the same population, i.e., they have the same mean. For example five fertilizers are applied of four plots each of wheat and yield of wheat on each of the plot is given we may be interested in finding out whether the effect of these fertilizers on the yields is significantly different or in other words, whether the samples have come from the same normal population. The answer to this problem is provided by the technique of analysis of variance. Thus basic purpose of the analysis of variance is to test the homogeneity of several means.

12.1 Introduction
 
The design of the experiment includes (i) planning of the experiment (ii) obtaining the relevant information from it regarding the statistical hypothesis under study and (iii) making a statistical analysis of the data. Design of experiment may be defined as the logical construction of the experiment in which the degree of the uncertainty with which the inference is drawn may be well defined. It may also be redefined as the choice of treatments, the method of assigning treatments to experimental units and arrangement of experimental units in varies patterns to suit the requirement of particular problems, are combined known as the design of experiment.

In many situations, experiments may not known in advance which contrasts they wish to compare, or they may be interested in more than (m – 1) possible comparisons.  In many exploratory experiments, the comparisons of interest are fixed only after preliminary examinations of the data.
 
13.1 Scheffe’s Method for Comparing All Contrasts

Scheffe (1953) has proposed a method for comparing any all possible contrasts between treatment means.  In the Scheffe method, the type I error is at most a for any of the possible comparisons.

In the foregoing experiments performed either in CRD or RBD or LSD. We were primarily concerned with the comparison and estimation of the effects of a single set of treatments like varieties of paddy, different methods of cultivation etc., and such experiments which deal with one factor only may be called simple experiments. In factorial experiment the effects of several factors of variation are studied and investigated simultaneously in a single experiment, such experiments are known as factorial experiments. In these experiments an attempt is made to estimate the effects of each factors and also the interaction effects i.e. the variation in the effect of one factor as a result to different levels of other factors.  These experiments provide an opportunity to study not only the individual effects of each factor but also interactions.  In many biological and clinical trials factors are likely to have interaction. Therefore, factorial experiments are more informative in such investigations.

In factorial experiment it may sometime happen that certain factors require bigger plots than other for convenience of operation of the experiment.  For example, in an agricultural experiment involving two factors, say, irrigation and fertilizer, it is organizationally very inconvenient to apply different levels of irrigation to small neighbouring plots.  But there is no such difficulty for the application of different levels of fertilizer. To meet such situations it is desirable to have different sizes of the experimental units in the same experiment.

Introduction

A system is a simplified version of reality and is defined as a collection of interrelated parts that work together. Most systems share certain common characteristics. These common characteristics include the following:
Systems have a structure that is defined by its parts and processes. Systems are generalizations of reality.
Systems tend to function in the same way as nature. This involves inputs getting changed in a process and realization of new outputs.
The various parts of a system have functional as well as structural relationships between each other.
Owing to the functional relationships that exist between the parts there is a flow and transfer of some type of energy and/or matter. In case of information systems there is a flow of information in the structure.
The parts that make up a system show certain degree of integration. In other words the parts work well together.

Introduction

In a technology that consistently maintains a fantastic development curve with capabilities doubling up year-to-year, freezing the hardware specification is not easy or very meaningful. GIS (not geography) owes to its genesis and progress to the developments in information technology front. It may be asserted that any and all upgrade in the hardware industry will only add to its own ability to effectively process more and more data and handle more complex routines. The personal computers of today are seen to be adequate to run typical GIS software. A reference hardware specification is discussed here. GIS software development is an involved process but then the software developers have made good use of hardware capabilities available and have released upgrades consistently. Hardware developments are such that they defy a static spcification. However a reference specification as on 2009. An overview of various software packages used in the past as well as present is attempted. ESRI released a new version in 2002 and claimed that their ArcGIS is an “integrated collection of GIS software products for building a complete GIS that enable users to deploy GIS functionality wherever it is needed, in desktops, servers, or custom applications; over the Web; or in the field.” The details are discussed below. While a number of software overviews are available, this text is not aimed at providing a tutorial to any of the software packages nor do the comparisons made are to promote any particular software. Similarly, it is presumed that the reader is familiar with the hardware elements and functionality. Lot of information about the software packages are available on their respective web sites and the reader is exhorted to look for current, additional and in depth information required in the pages. A brief discussion on Liveware or human resource can be found at the end of the chapter.

Introduction

All spatial data stored in a GIS are associated with a map projection, either implicitly (so-called “unprojected” data stored with raw longitude, latitude coordinates) or explicitly (where the data have been transformed into a known map projection). Map projections are a particularly important feature of spatial data because they introduce error and distortion, since a sphere cannot be transformed to a plane without some stretching and twisting of the sphere’s surface.

The typical GIS package supports anywhere from 5 to 50 map projection types, and most projections may be modified or customized by varying their parameters. This flexibility is important for two reasons First, in order to use two or more layers together in a GIS for visualization or analysis, all data must be projected in the same way. Second, certain types of analyses, such as the measurement of area, are only valid when using certain types of projections (and conversely, are completely wrong when using an inappropriate projection).

Introduction

Data are the most critical element of a GIS. It is reported that more than two thirds of the time and expenditure to develop a GIS is spent on data collection and organization. Understanding and appreciating the criticality of data is of paramount significance to practice GIS. This chapter is complementary to database concepts chapter discussed in Level II and deals with the fundamentals to start with and the concepts of building an information organization.

Data - A Definition and Certain Points to Ponder

“Data” are defined as a body of facts or figures, which have been gathered systematically for one or more specific purposes.”

Introduction

The concept of database is the approach to information organization in computer-based data processing. (Date, 1995 and Everest, 1986).

A Geographic Information System is as good as the data contained in it. Accurate and efficiently organized data are critical to the durability of the system. Data collection, capture and organization will occupy much of the time in building a GIS solution. The analysis potential of a GIS will depend on the database. It is essential to understand database concepts to practice GIS. It is erroneous to view these GIS databases as mere stores of information. The database is used to abstract very specific sorts of information about reality and organize it in a way that will prove useful during subsequent analysis. The database should be viewed as a representation or model of the world developed for a very specific application.

Conversion of real world geographical variation into discrete objects is done through data models. They represent the linkage between the real world domain of geographic data and computer representation of these features. Data models discussed here are for representing the spatial information.

Introduction

Data input or capture is the operation of encoding the data and incorporating in the database. The creation of a clean error free digital database is a complex and critical task determining the very usefulness of a GIS. GIS data represent not only where the graphic or cartographic features occur, but also the associated attributes of them.

Data input to a geographical information system involves

(a) Entering the spatial data (digitizing);

(b) Entering the non-spatial, associated attributes and

(c) Linking the spatial to the non-spatial data.

At each stage necessary verification and checking procedures shall be deployed to ensure an error free database.

Introduction

The GIS database is a model of real world. It goes without saying that there is an inherent discrepancy between GIS database and the real world it represents. This is so because all models are fraught with cognitive issues and remain as approximations. To realize the objectives of a GIS, geographical data collected, entered and processed are to be sufficiently error free and reliable.

Errors can be statistical errors like mistakes, wrong inputs or faulty field surveys or system errors like excessive tolerances or high RMS error. The spatial database has further problems in the sense that the real world phenomena are not always intrinsically discrete. For instance where is the bank of a shallow river? Where is the boundary between two soil types? These are but a few questions revealing the fuzzy nature of geographic data. Many soil scientists, geographers have noticed that carefully drawn boundaries or iso-lines on maps in general give misrepresentations of change that are often gradual, vague or fuzzy. Traditional cartography as a medium of spatial data handling had a solution to such problems. But now when digital data are handled, they need to be stored with certain precision. Moreover, the spatial variation of natural phenomena is not just a local noise function or inaccuracy that can be removed by collecting more data or by increasing the precision measurement but is often a fundamental aspect of nature that occurs at all scales. (Goodchild, 1980; Mark and Aronson, 1984). This means residual unmapped variation is a remarkable problem in geographical information processing. It is therefore a prerequisite to fully understand how errors arise or propagated in GIS and what might be the effects of errors on output.

Topology building

Boolean operations

Query and model building

Customization

Introduction

At human scales, the world consists of objects, events, processes, and a background environment. Cognition is about knowledge: its acquisition, storage and retrieval, manipulation, and use by humans and other intelligent creatures. Cognition includes sensation and perception, thinking, imagery, reasoning and problem-solving, memory, learning, and language. Cognitive structures and process are part of the mind, which emerges from a brain and nervous system inside of a body that exists in a social and physical world. Spatial cognition deals with the cognition of spatial properties of the world, including location, size, distance, direction, shape, pattern, movement, and inter-object relations. GIS design will be largely influenced by spatial cognition.

Introduction

The term geographic information systems (GIS) has come to mean, variously, an industry, a product, a technology, and a science. As such, the term invokes different perceptions dependent on whether the viewpoint is that of the software developer, the system marketer, the data provider, the application specialist, or the academic researcher, among others. A newcomer to the field is likely to be bewildered by the multiple uses of the same term, or, seen alternatively, different definitions of the same term. For instance some find it distasteful to use of the conventional acronym GIS and prefer Geospatial Technology. With all the variety of perceptions predicting the future of GIS is a cumbersome business. The most certain inference we can gain from the available trends is, it is growing and is poised for further growth. Geotechnology along with Nanotechnology and Biotechnology is considered as of the three most promising growth industries for the near future.

Introduction

Famous French postmodern philosopher Jean Baudrillard commented on information once. “Information can tell us everything. It has all the answers. But they are answers to questions we have not asked, and which doubtless don’t even arise.” This seemingly paradox observation fits contemporary Internet age perfectly. Internet is a vast repository of information of all kinds and is bursting on its seams. The seeker needs to search with monumental grit and determination to have a full handle on the range of information available for any particular search. A search string for GIS returns more than 1185000 results, in a mere 0.26 seconds. Recommending a comprehensive resource list is not possible as new material gets continually added by way of additions and updates. However for a beginner the list given below will be handy. The flip side is GIS has innumerable resources and a broad range of content. Software vendors, organizations, individuals, universities and companies have provided a wealth of information. The readers are exhorted to make use of the materials to improve their understanding of the subject.