Geographic query and analysis

APPLICATION OF REMOTE SENSING AND

GEOGRAPHICAL INFORMATION SYSTEM IN

CIVIL ENGINEERING

Date:

INSTRUCTORS

DR. MOHSIN SIDDIQUE

ASSIST. PROFESSOR

DEPARTMENT OF CIVIL ENGINEERING

Geographic query and analysis

How many houses are within 50m of this junction?

How many children live in this 100m grid square?

Which house holds falls within the floodplain?

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� Spatial analysis is a set of methods whose results change when the locationsof the objects being analyzed change

� Spatial analysis is the crux of GIS. Spatial analysts can reveal things that might

otherwise be invisible – it can make what is implicit explicit.

� Methods of spatial analysis can be very sophisticated, but they can also bevery simple

� There are many possible ways of defining spatial analysis, but all in one wayor another express the basic idea that information on locations is essential –that analysis carried out without knowledge of locations is not spatial analysis

� Spatial analysis can be used to further the aims of science, by revealingpatterns that were not previously recognized, and that hint at undiscoveredgeneralities and laws

Spatial Analysis3

� In the 1850s cholera was very poorlyunderstood

� An outbreak in London in 1854 in theSoho district was typical of the time,and the deaths it caused are mapped inFigure

� Dr. John Snow conceived the hypothesisthat cholera was transmitted through thedrinking of polluted water, rather thanthrough the air, as was commonlybelieved

� He noticed that the outbreak appearedto be centered on a public drinkingwater pump in Broad Street, and if hishypothesis was correct, the patternshown on the map would reflect thelocations of people who drank thepump’s water

Dr. John Snow and the causes of cholera

Dr. John Snow and the causes ofcholera in London

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� There are nine methods for testing spatial relations between geometric objects. Each takes as input two geometries and evaluates whether the relation is true or not.

� 1. Equals – are the geometries the same.

� 2. Disjoint – do the geometries share a common point

� 3. Intersects – do the geometries intersect

� 4. Touches – do the geometries intersect at their boundaries

� 5. Crosses – do the geometries overlap

� 6. Within – do the geometries within another

� 7. Contains – does one geometry completely contain another

� 8. Overlaps – do the geometries overlap

� 9. Relate – are the intersections between the interior, boundary or exterior of the geometries.

Spatial relations and analysis on geometric objects5

� Seven methods support spatial analysis on these geometries:

� 1: Distance – determines the shortest distance between any two points in twogeometries.

� 2: Buffer – returns a geometry that represents all the points whose distancefrom the geometry is less than or equal to a user defi ned distance.

� 3: Convex hull – returns a geometry representing the convex hull of ageometry (convex hull is the smallest polygon that can enclose anothergeometry without any concave areas).


� 4: Intersection – returns a geometry that contains just the points common to both input geometries.

� 5: Union – returns a geometry that contains all the points in both input geometries.

� 6: Difference – returns a geometry containing the points that are different between the two geometries.

� 7: SymDifference – returns a geometry containing the points that are in either of the input geometries, but not both.


Spatial relations and analysis on geometric objects

Examples of possible relations for two geographic database.

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Examples of spatial analysis methods on geometries.

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Examples of spatial analysis methods on geometries.

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� Queries and reasoning

� Measurements

� Transformations

� Descriptive summaries

� Optimization techniques

� Hypothesis testing

Types of Spatial Analysis11

� Queries and reasoning are the most basic of analysis operations, in whichthe GIS is used to answer simple questions posed by the user.

� No changes occur in the database, and no new data are produced.

� The operations vary from simple and well-defined queries like

� ‘how many houses are found within 1 km of this point’,

� to vaguer questions ‘which is the closest city to Lahore going east’, wherethe response may depend on the system’s ability to understand what theuser means by ‘going east’.

� Interrogations/queries may involve pointing at a map, typing a question orpulling down a menu and clicking some buttons or sending formal SQL requestto database.

� The term reasoning encompasses a collection of methods designed to respond to more complex forms of query and interrogation.

Queries and reasoning12

� Measurements are simple numerical values that describe aspects ofgeographic data.

� They include measurement of simple properties of objects, like

� length,

� area, or

� shape,

� and of the relationships between pairs of objects, like distance ordirection.

Measurements13

� Transformations are simple methods of spatial analysis that change datasets, combining them or comparing them to obtain new datasets, and eventually new insights.

� Transformations use simple geometric, arithmetic, or logical rules, and they include operations that convert raster data into vector data, or vice versa.

� They may also create fields from collections of objects, or detect collections of objects in fields.

� Buffering

� One of the most important transformations available to the GIS user is the buffer operation called buffering

� Given any set of objects, which may include points, lines, or areas, a buffer operation builds a new object or objects by identifying all areas that are within a certain specified distance of the original objects.

� Buffering is possible in both raster and vector GIS, in the raster case

Transformations14

Transformations

Buffers of constant width drawn around a point, line and a polygon

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Transformations

A raster generalization of the buffer function where changes may becontrolled by some variable (example is of travel speed, whose value isrecorded in every raster cell)

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� Point in polygon: In its simplestform, the point in polygon operationdetermines whether a given pointlies inside or outside a givenpolygon.

� In more elaborate forms there maybe many polygons, and many points,and the task is to assign points topolygons.

� If the polygons overlap, it is possiblethat a given point lies in one, many,or no polygons, depending on itslocation.

Transformations

The point in polygon problem.

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� Polygon overlay is similar to point in polygon transformation in the sense thattwo sets of objects are involved, but in this case both are polygons.

� It exists in two forms, depending on whether a field or discrete objectperspective is taken.

� From the discrete object perspective, the task is to determine whether twoarea objects overlap, to determine the area of overlap, and to define thearea formed by the overlap as one or more new area objects

� This operation is useful to determine answers to such queries as:

� How much area lies in the shaded zone?

� How much of this land parcel is shaded but not the white polygon?

� What proportion of the land area outside the shaded but inside the whitepolygon?

Transformations18

� From the field perspective the task issomewhat different.

� Figure shows two datasets, bothrepresentations of fields–onedifferentiates areas according toland ownership, and the otherdifferentiates the same regionaccording to land cover class.

� There are numerous queries thatrequire simultaneous access to both datasets, for example:

� ⇒⇒⇒⇒ What is the total area of land owned

by A and with land cover class X?

� ⇒⇒⇒⇒ Where are the areas that is owned by

C and have land cover class Y?

� ⇒⇒⇒⇒ What is the land cover class and who

is the owner of the point indicated by

the user?

Transformations

Polygon overlay in the field case. Where a

dataset representing two types of land

cover. It is overlaid on a dataset showing

three land parcels owned by three

different persons. The overlay result will be

a single dataset in which every point

is identified with one land cover type and

one ownership type. There will be five

polygons, as land over X intersects two

ownership types and land cover y

intersects with three.

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� Spatial interpolation is a pervasive operation in GIS.

� Although it is often used explicitly in analysis, it is also used implicitly, invarious operations such as the preparation of a contour map display, wherespatial interpolation is invoked without the user’s direct involvement.

� Spatial interpolation is a process of intelligent guesswork, in which theinvestigator attempts to make a reasonable estimate of the value of a field atplaces where the field has not actually been measured.

� Spatial interpolation is an operation that makes sense only from the fieldperspective.

Spatial interpolation20

� Spatial interpolation finds applications in many areas:

� In contouring, when it is necessary to guess where to place contours in

between measured locations.

� In estimating the elevation of the surface in between the measuredlocations of a DEM.

� In estimating rainfall, temperature, and other attributes at places that are

not weather stations, and where no direct measurements of thesevariables are available.

� In resampling rasters, the operation that must take place whenever raster

data must be transformed to another grid.

� Two commonly used methods of spatial interpolation are inverse distance weighting (IDW) and Kriging

Spatial interpolation21

� Inverse distance weighting (IDW)

is the workhorse of spatialinterpolation, the method that ismost often used by GIS analysts.

� It employs the Tobler law byestimating unknown measurementsas weighted averages over theknown measurements at nearbypoints, giving the greatest weight tothe nearest points.

� IDW provides a simple way ofguessing the values of a field atlocations where no measurement isavailable.

Inverse distance weighting

Notation used in the equations defining spatial interpolation.

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Inverse distance weighting

IDW interpolation

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� Kriging: Of all of the commonmethods of spatial interpolation it isKriging that makes the mostconvincing claim to be grounded ingood theoretical principles.

� The basic idea is to discoversomething about the generalproperties of the surface, asrevealed by the measured values,and then to apply these propertiesin estimating the missing parts of thesurface.

� Smoothness is the most importantproperty, and it is operationalizedin Kriging in a statisticallymeaningful way.

Kriging

A semivariogram, here each

cross represents a pair of points.

The solid circles are obtained by

averaging within the ranges of

the distance axis. The dashed

line is the best fit to the five

points.

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� Density estimation is in many ways the logical twin of spatial interpolation –it begins with points, and ends with a surface.

� But conceptually the two approaches could not be more different, becauseone seeks to estimate the missing parts of a field from samples of the fieldtaken at data points, while the other creates a field from discrete objects.

Density estimation

Two identical datasets but with different representations.A – represents a field of atmospheric temperature recorded at nine sample points.

B – nine discrete objects representing population of different settlements in thousands.Spatial interpolation is appropriate for case A, while for case B density estimation is

suitable.

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� Density estimation could be applied to any type of discrete spatial object, it is most often applied to the estimation of point density, and that is the focus here.

� The most obvious example is the estimation of population density, and but it could be equally well applied to the density of different kinds of diseases, or animals, or any other set of well-defined points.

Density estimation

Density estimation using two different distance parameters in therespective kernel functions, displaying smoother and less peaked nature ofthe surface that results from the larger distance parameter.

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� Descriptive summaries attempt to capture the essence of a dataset in one ortwo numbers.

� They are the spatial equivalent of the descriptive statistics commonly used instatistical analysis, including the mean and standard deviation.

� Centers: To analyze the numerical summaries generally we measure bymethods of central tendency.

� Like mean is one method which is broadly citing the average of dataseries, similarly median, where the value is as such that one half of thenumbers are larger and one half are smaller.

� Dispersion: Central tendency is the obvious choice if a set of numbers are tobe summarized in a single value, but where there is opportunity for a secondsummary value standard deviation or the variance is often used.

� Standard deviation and variance are more appropriate measures ofdispersion than the range because as averages they are less sensitive tothe specific values of the extremes

Descriptive summaries27

� Histograms and Pie Charts: Histograms (bar graphs) and pie charts are twoof many ways of visualizing the content of a geographic database.

� A histogram shows the relative frequencies of different value of an attributeby ordering them on the X axis and displaying frequency through the lengthof a bar parallel to the Y axis.

� Attributes should have interval or ratio properties, although ordinal propertiesare sufficient to allow the values to be ranked and histogram based onordinal data is useful representation.

� A pie chart is useful for nominal data and is used to display the relative frequencies of distinct values, with no necessity for ranking.

� Pie charts are also useful in dealing with attributes measured on cyclic scales.

� Both take a single attribute and organize its values in a form that allow quick comprehension.


� Scatterplots are useful visual summaries of relationships between attributes. It display the value of one attribute plotted against the other.

� If both sets of attributes belong to the same objects then the construction of a scatter plot is straight.

� Spatial Dependence: The fundamental problem of spatial analysis is selecting appropriate digital representations from the real world.

� The Tobler’s first law of geography states that everything is related to everything else but near things are more related than distant things.

� A dataset can exhibit positive spatial dependence at one scale but negative at another scale.

� Spatial dependence is a very useful descriptive summary of geographic data and a fundamental part of its nature


� Fragmentation and Fractional Dimension: In GIS, maps may show manypatches with each patch representing an area of uniform class and this maybe bounded by patch of different class.

� For example, a soil map where we may be interested in the degree to whichthe landscape can be fragmented (meaning breaking in small or largepatches).

� Fragmentation statistics provide the numerical basis for this purpose. Here wecan analyze the number of patches, their shape or size etc, as a way ofsummarizing the geographic details.

� The concept of fractals is used as a way of summarizing the relationshipbetween apparent length and level of geographic detail in the form offractional dimensions.


� Optimization techniques are normative in nature, designed to select ideallocations for objects given certain well-defined criteria.

� They are widely used in market research, in the package delivery industry,and in a host of other applications.

� Optimization is a prime example of GIS utility to support spatial decisions. It can be by many ways like optimum location of points, routing on a network, selection of optimum paths across continuous space, locating facilities etc.

� The methods of optimization also divide between those that are designed

� to locate points and routes on network

� and those designed to locate points and routes in continuous space without respect to the existence of roads or other links. E.g., Point Locations, Routing Problems, and Optimum Paths

Optimization techniques31

� Point Locations: It is an instance of location in continuous space andidentifying location that minimizes total distance with respect to a number ofpoints.

� The analogous problem on a network would involve finding that location onthe network which minimize total distance to a number of points, also locatedon the network, using routes that are considered on the network using routesthat are constrained to the network.

� Location allocation involves two types of decisions – where to locate and howto allocate demand for service.


� Routing Problems: This is another area of optimization where routing andscheduling or decisions about he optimum tracks are considered.

� At the root of all routing problem is the shortest path, the path through thenetwork between a defined origin and destination that minimizes distance ortravel time.

� Attributes such as length, travel speed, restrictions on travel direction and levelof congestion are taken into account.

� A GIS can be very effective at solving routing problems because it is able toexamine vast numbers of possible solutions quickly.


� Optimum Paths: Here the concern is for finding optimum path acrosscontinuous space for linear facilities like highways, pipelines or even airlinepath etc.

� Again emphasis would be on shortest route may be to save fuel, time oravoiding the restrictions if there are any.

� These are normally sorted in raster, where each cell may be assigned afriction value, equal to the cost or time associated with moving across the cellin the horizontal or vertical directions.


� Hypothesis testing focuses on the process of reasoning from the results of alimited sample to make generalizations about an entire population.

� It allows us, for example, to determine whether a pattern of points could havearisen by chance, based on the information from a sample.

� Hypothesis testing is based on two concepts – confidence limits and inferential tests, which are basically statistical testing.

Hypothesis testing35

Comments….

Questions….

Suggestions….

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I am greatly thankful to all the information sources(regarding remote sensing and GIS) on internet that Iaccessed and utilized for the preparation of presentlecture.

Thank you !

Geographic query and analysis

Engineering

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