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analisis data spasial
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Spatial DataAnalysis
Spatial DataAnalysis
Spatial Data Analysis
¬Distinguishing capabilities of GIS¬For spatial decision making¬Transform and combine spatial data into
useful information
Spatial Data Analysis
¬ Measurement basic spatial measurement x,y distance, area, etc
¬ Spatial Querydata retrieval on both geometric and attribute data
¬ Re(Classification)assign new classification codes
¬ Overlaycombine data layers and derive new information: topological and raster overlay
¬ Neighbourhoodevaluate the charateristics of surrounding area
¬ Network analysisthe connectivity of linear features
¬ 3D analystcalculating volume, generating profiles, determining watershed boundaries
Measurement Operation
¬Only geometric measurements¬Not attribute data measurement¬2D Spatial reference system
LIMITATIONS...
Measurements Operations - Vector
¬Measurement on individual features°Location°Length°Distance°Area size
Measurements Operations - Vector
¬Distance calculation
0,0 Xq Xp
Yq
Yp
X
Y
22 )()(),( qpqp YYXXqpdist −+−=
Measurements Operations - Vector
¬Stored as attributes in the tables¬Automatically updated when geometric
editing occurs, for example, polygon splitting¬Specify map projection and coordinate
systems beforehand¬The measurement is in the spheroid
units when data are in geographic units
Measurements Operations - Raster
¬Simpler because of the regularity of the cells¬Usually by counting the number of cells¬Cell resolution may differ horizontally
and vertically¬The anchor point is georeferenced and
in the lower-left corner of the raster
Spatial Data Analysis
¬ Measurement basic spatial measurement x,y distance, area, etc
¬ Spatial Querydata retrieval on both geometric and attribute data
¬ Re(Classification)assign new classification codes
¬ Overlaycombine data layers and derive new information: topological and raster overlay
¬ Neighbourhoodevaluate the charateristics of surrounding area
¬ Network analysisthe connectivity of linear features
¬ 3D analystcalculating volume, generating profiles, determining watershed boundaries
Spatial selection query
¬Define a query expression¬Execute the query¬Display the results
Basic procedure…
Spatial selection query
¬Specify the data layer¬Define selection objects by pointing or
drawing¬Specify the overlay operations (intersect,
meet, contain, etc.)¬Features within the selection objects are
selected and highlighted¬Display the attributes of the selected objects¬Answer questions: What is at … ?
Interactive spatial selection ...
Spatial selection query
Interactive spatial selection ...
Spatial selection query
¬Define a selection condition on the features attributes in a query language, such as SQL.¬Display the result both on the map and
in the attribute table¬Answers questions: Where are the
features with …?
By attribute condition ...
Spatial selection query
By attribute condition ... Area < 400000
Spatial selection query
¬Using logical connectives to combine atomic conditions into composite conditions:°AND°OR°NOT°Bracket pair ()
Area < 4000000 AND Landuse = 80Area < 4000000 OR Landuse = 80
Combining attribute conditions ...
Spatial selection query
Area < 4000000 ANDLanduse = 80
Combining attribute conditions ...
Spatial query
Basic steps:¬Select one or more features (using either spatial or
attribute selection) – selection objects.
¬Apply one of the spatial relationship operations to select other features¬Display the result both on the map and in the
attribute table
Using topological relationships ...
Spatial Relationships
Spatial query
¬Uses the containment relationships¬Can be applied to certain features types:
Select features inside selection objects ...
Spatial query
Select features...
Select all clinics in district “A”
Spatial query
¬All relationships except disjoint.¬Two polygons intersect if they share a
common area.¬Two line intersect if they share a common line
segment¬A line and a polygon intersect if the line is
partially or completely inside the polygon
Select features that intersect ...
Spatial query
Select features that intersect ...
Select all the roads that are partially or completely located in district “A”
Spatial query
¬Use the MEET relationship.¬Share common
boundaries.¬Apply only to line and
polygon features
Select features adjacent to selection objects ...
Industry area adjacent to the selection polygon
Selection of polygon
Spatial query
Select features based on their distance ...
Which roads are within 200 meters of a clinic
Spatial selection query
¬Also called buffer generation or proximity analysis.
Select features within or beyond a specified ...
#
Point Line Polygon
Spatial query
¬Generate buffer polygons.¬Apply containment relationship
operation to select features inside or outside the buffer polygons¬Display result
Spatial query Roads and banks
Select highway class roads
Create buffer along highway
Select banks within the buffer polygons
Example:Select all the banks within 200 meters from highway class roads
Spatial Data Analysis
¬ Measurement basic spatial measurement x,y distance, area, etc
¬ Spatial Querydata retrieval on both geometric and attribute data
¬ Re(Classification)assign new classification codes
¬ Overlaycombine data layers and derive new information: topological and raster overlay
¬ Neighbourhoodevaluate the characteristics of surrounding area
¬ Network analysisthe connectivity of linear features
¬ 3D analystcalculating volume, generating profiles, determining watershed boundaries
(Re)Classification
¬Assign codes based on specific attributes¬Reduce the number of classes and
eliminate details¬Useful for revealing spatial patterns¬Reclassify data in different systems or
for different purposes
(Re)Classification - procedure
Example:¬Soil types reclassified into soil suitability for
agricultural purpose.¬House hold income classification:°Low°Below average°Average°Above average°High
(Re)Classification - mergeFive classes of house hold income with original polygons intact
Five classes of house hold income with original polygons merged (boundary dissolved) in the same categories
(Re)Classification
¬Differences on vector and raster data
No. because only pixel attributes are change
Yes. For example, two neighbour polygons merged into one
Geometric or topological change
Classification attribute may be nominal, ordinal or interval/ratio
Classification attribute is usually nominal or ordinal
Input data source
RasterVector
(Re)Classification
¬User indicates the classification attribute(s)¬User specified the classification method:°The number of output classes°The correspondence between the original attribute
values and the new attribute values°Called classification table
User controlled classification ...
(Re)Classification
User controlled classification ...
(Re)Classification
¬ User specifies the no. or output classes¬ Computer decides the class break points¬ Equal interval:
class interval = (Vmax – Vmin)/n¬ Equal frequency (quantile):°Maintain equal or nearly equal no. of features in each
category in the output
Automatic classification ...
(Re)Classification - automatic
Equal interval (5 classes)
Equal frequency (5 classes)
(Re)Classification - raster
1.1 1.2 1.4 2.8 8.2
4.1 4.5 5.6 4.3 9.0
4.5 3.5 3.2 2.1 1.3
4.3 5.2 6.0 8.5 8.9
4.3 4.4 1.2 1.1 1.9
(a)
1 1 1 2 8
4 4 5 4 9
4 3 3 2 1
4 5 6 8 8
4 4 1 1 1
(b)
Lower limit
Upper limit
New Category
1.0 1.9 1 2.0 2.9 2 3.0 3.9 3 4.0 4.9 4 5.0 5.9 5 6.0 6.9 6 7.0 7.9 7 8.0 8.9 8 9.0 9.9 9
Spatial Data Analysis
¬ Measurement basic spatial measurement x,y distance, area, etc
¬ Spatial Querydata retrieval on both geometric and attribute data
¬ Re(Classification)assign new classification codes
¬ Overlaycombine data layers and derive new information: topological and raster overlay
¬ Neighbourhoodevaluate the characteristics of surrounding area
¬ Network analysisthe connectivity of linear features
¬ 3D analystcalculating volume, generating profiles, determining watershed boundaries
Overlay functions
¬Combines several data layers into one.¬New spatial elements are usually created¬All map layers must be georeferenced in the
same system¬Both on vector and raster data
Overlay operations – Vector data
¬Topologically overlay¬ Involves complicated geometric calculations
to create new topology¬Spatial features are combined¬New attributes are assigned to each new
feature, such as area, parameters¬The attributes from the input data layers are
kept in the output
Overlay operations
Overlay operations - Union
¬ All the features in the two input data source are kept in the output
¬ Applies only to polygon features
Overlay operations - Intersect
¬ Only the features inside the common area of the two input data are kept in the output.
¬ One input data can be point, line or polygon feature type, the other must be a polygon data set.
Overlay operations - Identity
¬ All features of the input coverage, as well as those features of the Identity coverage that overlap the input coverage, are preserved in the output coverage.
¬ One input data can be point, line or polygon features type, the other must be a polygon data set.
Overlay operations - CLIP
¬ Extracts those features from an input coverage that overlap with a clip coverage.
¬ No combination of attributes.
Overlay operations - ERASE
¬ Erases the input coverage features that overlap with the erase coverage polygons.
¬ No combinations of attributes.
Overlay operations - UPDATE
¬ Replace the input coverage areas with the update coverage polygons using a cut-and-paste operation.
Overlay operations - RASTER
¬ New cell values are calculated using calculus – map algebra
¬ Performed on cell-by-cell basis¬ No geometric calculation
Output_raster_name := raster_calculus_expression
Overlay operations - RASTER
Arithmetic operators¬ +, -, *, /¬ MOD (modulo division)¬ DIV (integer division)¬ Goniometric operators: sin, con, tan, asin, acos, atan.
Raster2 := Raster1 * 5
Overlay operations - RASTER
Overlay operations - RASTER
¬ Comparison operators° <, <=, =, >=, >, <>
¬ Logical operators° AND, OR, NOT, XOR (exclusive)
a XOR b is true if either a or b is true
¬ The cell values of the output raster is either true or false
Raster3 := Raster1 <> Raster2
Overlay operations - RASTER
Example of logical expressions
Overlay operations - RASTER
Overlay operations - RASTER
Output_raster := IFF(condition, then_expression, else_expression
Conditional expression
Overlay operations - RASTER
Overlay using a decision table• for complicated conditional expression
Spatial Data Analysis
¬ Measurement basic spatial measurement x,y distance, area, etc
¬ Spatial Querydata retrieval on both geometric and attribute data
¬ Re(Classification)assign new classification codes
¬ Overlaycombine data layers and derive new information: topological and raster overlay
¬ Neighbourhoodevaluate the characteristics of surrounding area
¬ Network analysisthe connectivity of linear features
¬ 3D analystcalculating volume, generating profiles, determining watershed boundaries
Neighbourhood functions
Find out the characteristics of the neighbourhood of a location°Proximity computationÜBuffer zone generationÜThiessen polygon generation
°Spread computationÜData retrieval on both geometric and attribute data
°Seek computationÜAssign new classification codes
Neighbourhood functions
Neighbourhood functions
¬ Thiessen polygon generation° Partition the plane into polygons so that each polygon
contains location that are closer to the polygon’s midpoint than to any other midpoints.
° Easier to construct thiessen polygons based on Delanuay triangulation
Neighbourhood functions
¬ Spread computation
Neighbourhood functions
¬ Seek computation
Spatial Data Analysis
¬ Measurement basic spatial measurement x,y distance, area, etc
¬ Spatial Querydata retrieval on both geometric and attribute data
¬ Re(Classification)assign new classification codes
¬ Overlaycombine data layers and derive new information: topological and raster overlay
¬ Neighbourhoodevaluate the characteristics of surrounding area
¬ Network analysisthe connectivity of linear features
¬ 3D analystcalculating volume, generating profiles, determining watershed boundaries
Network analysis
¬Model physical network features, road, river, telecommunication etc.¬Characteristics:°Move resources from one place to another on the
network: goods delivery, electricity power transmission etc.
¬Network problem:°Connectivity°movement
Network analysis – data model
¬ Consists of lines and nodes.¬ A line begins and ends at nodes -from- and to-nodes.
Network analysis – data model
¬Point features°If it is not located directly on a line or a
node, it is snapped to the nearest node or line.°In the case of line, the line is split and a
new node is added
Network analysis – data model
¬Line direction°Every line has a direction.°It’s defined in from-node (start-node) to to-
node (end-node) direction.°Line direction is an important element in
network analysis, specially in defining different attributes for different directions of the lines
Network analysis – data model
¬Planar network data model°Every intersection between line has a
node. Line has a direction.°Difficult in modeling some applications,
such as multiple-grade crossing (underpass/overpass)°Non-planar network data model is more
suitable for network analysis.
Network analysis – data model
¬Connectivity°Two lines are directly connected if they
share a common node.°Two lines are indirectly connected if a path
can be found that connects the two lines.°Two lines are not connected if no path can
be generated between them.°Concerns not only the geometric topology,
but also semantic relationships.
Network analysis
¬Optimum path finding: generates a least cost-path on a network between a pair of predefined locations based on both geometric and attribute data¬Network partitioning: assigns network
elements (nodes and the whole or a portion of lines) to different locations based on predefined criteria
Network analysis
¬ Required data° A set of network elements (line segments and nodes) and
topological structure that defined the connectivity, for example, a water supply network
° The types and the capacity of the resources to be transported, for example, the amount of water that can be produced at the water plant and distributed through a network
° The location of the start and the end points of the movement, for example, the location of the water plant as the supply point and the locations of customers as the consumption points of the water along the water pipes.
° The constraints associated with the resources movement, for example, the minimum pressure level of water supply, traffic speed limit, no right or left turn restrictions on road intersections, the maximum volume of run-off water in a sewage system, etc.
Network analysis – Path finding
¬Establish the least-cost path between two nodes¬Define a sequence of lines for traverse that
cost min. compared to any other alternative path
Network analysis
¬Cost factors°Also called impedance.°One of the attributes in the feature attribute table.°Length, travel time, etc.°The least-cost path is the one that has the minimal
value of the total cost between two nodes.
Network analysis
¬ Cost factors° The cost can be defined on both lines and nodes.° For line, the cost can be same or different along and against
the line direction.° The cost on nodes is used to define the turns
Network analysis
¬ Minimum-cost path° In principle, it should calculate the total costs for all the
possible paths between the origin and the destination points. Then select the minimum-cost path.
° To reduce the computation time, different algorithms are applied, such as Dijkstra’s.
Network analysis
¬Ordered path finding°User defines the origin, the destination and the
order in which the intermediate stops to be visited.
¬Non-ordered path finding°User defines the origin, the destination and the
location of the intermediate stops to be visited°The computer determines the order in which the
intermediate stops to be visited
Optimal path finding Non-ordered path finding
Network analysis - partitioning
¬Assign portion of network elements to a location¬A set of criteria or constrains must be defined
to decide if° An element is assigned to a location° To which location it should be assigned
¬Two types of partitioning° Allocation° Trace
Network analysis - partitioning
¬Allocation° Assigns a portion of network to a resource center° The resource center has certain capacity to be distributed
around° The network elements (lines and nodes) have an attribute
representing the consumption of the resource° The resource center is modeled as node and its capacity is
represented as an attribute
¬ Examples:°Water plants and distribution network.° Schools and the service area
Network analysis - partitioning
¬Allocation°The result of allocation is a portion of connected
network dispersed from the center°The extent is defined when all the capacity of the
center is exhausted°User many specify a max. amount of consumption
for each branch of the network paths.
Network analysis - partitioning
Allocation – an example
Network analysis - partitioning
¬Trace°Determines which portion of network is connected
to a trace origin°The line direction is important
Network analysis - partitioning
¬Accessibility°Determine how easily a location can access the
facilities around it°Each activity center is assign an index to reflect its
attraction level°The accessibility operation calculates the
accessibility a location by taking into consideration of both the distance to and the index of each activity center