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WMO/FAO Training Workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the SADC Spatial Data Analysis Thelma A. Cinco Senior Weather Specialist ,PAGASA Resource Person Philippines Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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Spatial analysis Spatial analysis is the vital part of GIS. Spatial analysis in GIS involves three types of operations attribute query (also known as non-spatial), spatial query and generation of new data sets from the original databases. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Spatial Data Analysis Representation of reality**

Purpose is to understand, describe, predict the real world scenarios Gives a simplified , manageable view of the real world Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Attribute Query ArcView’s Query Builder Command line Query (Arc/Info)**

([State name] = “California” or “New York”) ([City name] = “San*”) New Set Add to Set Select from Set Command line Query (Arc/Info) find in states where state_name = ‘California’ <1 record in result> calculate in states population_density = population / area <50 records in result> restrict in states where population_density > 1000 <20 records selected in result>> Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Attribute Query Using Boolean Logic**

Data retrieval is done by applying the rules of Boolean logic to operate on the attributes. Boolean algebra uses the operators AND, OR, XOR and NOT to see whether a particular condition is true or not. Ex. TYPE = ‘ASPHALT’ AND LENGTH = 4000 AND LANES = 4 Simple Boolean logic is often portrayed visually in the form of Venn diagrams Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**VENN DIAGRAM A AND B = Result T T T T F F F T F F F F A B A B A AND B**

A NOT B A XOR B = Result T T F T F T F T T F F F A OR B = Result T T T T F T F T T F F F A B A B A OR B A XOR B A B A B C C (A AND B) OR C A AND (B OR C) Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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Spatial Search/Query Overlay is a spatial retrieval operation that is equivalent to an attribute join. Buffering is a spatial retrieval around points, lines, or areas based on distance. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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Find all houses within a certain area that have tiled roofs and five bedrooms, then list their characteristics. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Buffering can be constructed around a point, line or area.**

Buffering algorithm creates a new area enclosing the buffered object. The applications of this buffering operations include, for example, identifying protected zone around lakes and streams, zone of noise pollution around highways, service zone around bus route, or groundwater pollution zone around waste site. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)**

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Spatial Overlay An operation that merges the features of two coverage layers into a new layer and relationally joins their feature attribute table. When overlay occurs, spatial relationships between objects are updated for the new, combined map. In some circumstances, the result may be information about relationships (new attributes) for the old maps rather than the creation of new objects. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**GIS usage in Spatial Analysis**

GIS operational procedure and analytical task that are particularly useful for spatial analysis Single layer operations Multi layer operations/ Topological overlay Spatial modeling Geometric modeling Calculating the distance between geographic features Calculating area, length and perimeter Geometric buffers. Point pattern analysis Network analysis Surface analysis Raster/Grid analysis Fuzzy Spatial Analysis Geostatistical Tools for Spatial Analysis While basic spatial analysis involves some attribute queries and spatial queries, complicated analysis typically require a series of GIS operations including multiple attribute and spatial queries, alteration of original data, and generation of new data sets. The methods for structuring and organizing such operations are a major concern in spatial analysis. An effective spatial analysis is one in which the best available methods are appropriately employed for different types of attribute queries, spatial queries, and data alteration. The design of the analysis depends on the purpose of study. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Point pattern analysis**

It deals with the examination and evaluation of spatial patterns and the processes of point features. Distribution of an endangered species examined in a point pattern analysis . Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Vector Based Spatial Data Analysis**

There are multi layer operations, which allow combining features from different layers to form a new map and give new information and features that were not present in the individual maps. Topological overlays: Selective overlay of polygons, lines and points enables the users to generate a map containing features and attributes of interest, extracted from different themes or layers. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Point-in-polygon overlay Map overlay - point in polygon**

Topological overlays Point-in-polygon overlay Point-in-polygon algorithm overlays point objects on areas and compute "is contained in" relationship. The result is a new attribute for each point specifying the polygon it belongs to. Map overlay - point in polygon Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Line-on-Polygon Overlay**

Line-on-polygon algorithm overlays line objects on area objects and compute "is contained in" relationship. Lines are broken at each area object boundary to form new line segments and new attributes created for each output line specifying the area it belongs to. Output is line coverage with additional attribute. No polygon boundaries are copied. New arc-node topology is created. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Polygon-on-Polygon Overlay**

Polygon-on-polygon algorithm overlay two layers of area objects. Boundaries of polygons are broken at each intersection and new areas are created. During polygon overlay, many new and smaller polygons may be created, some of which may not represent true spatial variations. Polygon-in-polygon overlay: Output is polygon coverage. Coverages are overlaid two at a time. There is no limit on the number of coverages to be combined. New File Attribute Table is created having information about each newly created feature. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Sliver or spurious polygons**

Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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Overlay operation Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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Network analysis: Designed specifically for line features organized in connected networks, typically applies to transportation problems and location analysis such as school bus routing, passenger plotting, walking distance, bus stop optimization, optimum path finding etc. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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Surface analysis Deals with the spatial distribution of surface information in terms of a three-dimensional structure. The distribution of any spatial phenomenon can be displayed in a three dimensional perspective diagram for visual examination. Surface analysis deals with the spatial distribution of surface information in terms of a three-dimensional structure. The distribution of any spatial phenomenon can be displayed in a three dimensional perspective diagram for visual examination. A surface may represent the distribution of a variety of phenomena, such as population, crime, market potential, and topography, among many others. The perspective diagram in represents topography of the terrain, generated from digital elevation model (DEM) through a series of GIS-based operations in surface analysis. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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Grid analysis Involves the processing of spatial data in a special, regularly spaced form. The following illustration shows a grid-based model of fire progression. The darkest cells in the grid represent the area where a fire is currently underway. A fire probability model, which incorporates fire behavior in response to environmental conditions such as wind and topography, delineates areas that are most likely to burn in the next two stages. Lighter shaded cells represent these areas. Fire probability models are especially useful to fire fighting agencies for developing quick-response, effective suppression strategies. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Geostatistical Tools For Spatial Analysis**

Geostatistics studies spatial variability of regionalized variables: Variables that have an attribute value and a location in a two or three-dimensional space. Tools to characterize the spatial variability are: Spatial Autocorrelation Function and Variogram. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Point interpolation Interpolation to a Grid Assumption**

The influence of one known point over an unknown point increases as distance between them decreases. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Accuracy of Interpolation**

Depends on accuracy, number and distribution of the known points used in the calculation Depends on how accurate the mathematical function used correctly models the phenomenon. As the assumptions of the model are more severely violated, the interpolation results become less accurate. No matter which interpolator is selected, the more input points and the greater their distribution, the more reliable the results. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Interpolation Using Thiessen Method**

graphical technique which defines the individual 'regions of influence' around each of a set of points. Thiessen polygon boundaries are the perpendicular bisectors of straight lines drawn between two neighboring points Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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Thiessen Polygon Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Interpolation Using Neighborhood Model**

Inverse-Distance theory dictates that: the value of X > 58 the value of X < 97 the value of X is closer to 58 than 97. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Types of “Neighborhood” with IDW**

Nearest n Neighbors in this example, n = 3 this method is not effective when there are clusters of points Fixed Radius a radius is selected points are selected only if they lie within that fixed radius Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Using IDW in Spatial Analyst**

Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Interpolation Using the Spline Method**

The Spline interpolator fits a minimum-curvature surface through input points “Rubber sheet fit” Uses a piecewise polynomial to provide a series of patches resulting in a surface that has continuous first and second derivatives Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Interpolation Using the Spline Method**

output data structure is points on a raster note that maxima and minima do not necessarily occur at the data points is a local interpolator can be exact or used to smooth surfaces computing load is moderate best for very smooth surfaces poor for surfaces which show marked fluctuations, this can cause wild oscillations in the spline Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Using the Spline Method in Spatial Analyst**

Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Interpolation Using the Spline Method**

Two options: Regularized: usually produces smoother surfaces; typical values are 0, 0.001, 0.01, 0.1, and 0.5. Tension: higher values entered for the weight parameter results in somewhat coarser surfaces, but surfaces that closely conform the control points; typical values are: 0, 1, 5, and 10. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Interpolation Using Kriging**

Based on regionalized variable theory that assumes that the same pattern of variation can be observed at all locations on the surface. the basis of this technique is the rate at which the variance between points changes over space this is expressed in the variogram which shows how the average difference between values at points changes with distance between points This method produces a statistically optimal surface, but it is very computationally intensive. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Raster Based Spatial Data Analysis**

In raster analysis, geographic units are regularly spaced, and the location of each unit is referenced by row and column positions. Because geographic units are of equal size and identical shape, area adjustment of geographic units is unnecessary and spatial properties of geographic entities are relatively easy to trace. All cells in a grid have a positive position reference, following the left-to-right and top-to-bottom data scan. Every cell in a grid is an individual unit and must be assigned a value. Depending on the nature of the grid, the value assigned to a cell can be an integer or a floating point. When data values are not available for particular cells, they are described as NODATA cells. NODATA cells differ from cells containing zero in the sense that zero value is considered to be data. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Advantages of using the Raster Format in Spatial Analysis**

Efficient processing: Because geographic units are regularly spaced with identical spatial properties, multiple layer operations can be processed very efficiently. Numerous existing sources: Grids are the common format for numerous sources of spatial information including satellite imagery, scanned aerial photos, and digital elevation models, among others. Different feature types organized in the same layer: For instance, the same grid may consist of point features, line features, and area features, as long as different features are assigned different values Efficient processing: Because geographic units are regularly spaced with identical spatial properties, multiple layer operations can be processed very efficiently. Numerous existing sources: Grids are the common format for numerous sources of spatial information including satellite imagery, scanned aerial photos, and digital elevation models, among others. These data sources have been adopted in many GIS projects and have become the most common sources of major geographic databases. Different feature types organized in the same layer: For instance, the same grid may consist of point features, line features, and area features, as long as different features are assigned different values Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Grid Format Disadvantages**

Data redundancy: When data elements are organized in a regularly spaced system, there is a data point at the location of every grid cell, regardless of whether the data element is needed or not. Resolution confusion: Gridded data give an unnatural look and unrealistic presentation unless the resolution is sufficiently high. Conversely , spatial resolution dictates spatial properties. For instance, some spatial statistics derived from a distribution may be different, if spatial resolution varies, which is the result of the well-known scale problem. Cell value assignment difficulties: Different methods of cell value assignment may result in quite different spatial patterns. Data redundancy: When data elements are organized in a regularly spaced system, there is a data point at the location of every grid cell, regardless of whether the data element is needed or not. Although, several compression techniques are available, the advantages of gridded data are lost whenever the gridded data format is altered through compression. In most cases, the compressed data cannot be directly processed for analysis. Instead, the compressed raster data must first be decompressed in order to take advantage of spatial regularity. Resolution confusion: Gridded data give an unnatural look and unrealistic presentation unless the resolution is sufficiently high. Conversely, spatial resolution dictates spatial properties. For instance, some spatial statistics derived from a distribution may be different, if spatial resolution varies, which is the result of the well-known scale problem. Cell value assignment difficulties: Different methods of cell value assignment may result in quite different spatial patterns. Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Grid Operations used in Map Algebra**

Map Algebra performs following four basic operations: Local functions: that work on every single cell, Focal functions: that process the data of each cell based on the information of a specified neighborhood, Zonal functions: that provide operations that work on each group of cells of identical values, and Global functions: that work on a cell based on the data of the entire Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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Reclassification Reclassification is to reassign new thematic values or codes to units of spatial feature, which will result in merging polygons. A set of "reclassify attributes", "dissolve the boundaries" and "merge the polygons" are used frequently in aggregating area objects Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Grid operations Focal Function Local Function Zonal Function**

Global Function Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Raster Overlay Replace all 0’s in B A B with data from A**

Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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Raster Data Buffering Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Some important raster analysis operations**

Renumbering areas in a grid file Characterizing Terrain Feature Performing a Cost surface analysis Performing on Optimal Path analysis Performing proximity Search Creating Variable-Width Buffers Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**-Altering attribute values without changing geometry.**

Classification A-B : agriculture soil C-E : non agriculture soil Soil map Agricultural soil map -Altering attribute values without changing geometry. -to see new pattern and connection Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Characterizing Terrain Feature**

Identifying Convex and Concave features by deviation from the trend of the terrain. Figures from Asia Asian Institute of Technology Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Source:Lecture notes from the Asian Institute of Technology

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**Characterizing Terrain Feature**

2-D, 3-D and draped displays of terrain slope Figures from Asia Asian Institute of Technology Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Source:Lecture notes from the Asian Institute of Technology

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**Routing and Optimal Paths**

The steepest downhill path from the Substation over the Accumulated Cost surface identifies the Most referred Route minimizing visual exposure to houses. Figures from Asia Asian Institute of Technology Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Source:Lecture Notes from Asian Institute of Technology

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**Routing and Optimal Paths**

Alternate routes are generated by evaluating the model using weights derived from different group perspectives Figures from Asia Asian Institute of Technology Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC) Source:Lecture notes from the Asian Institute of Technology

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**Lecture from Asian Institute of Technology**

References Spatial Data Analysis by P.L. Raju Intro to Interpolation Engr. Bobby Crisostomo, NAMRIA Lecture from Asian Institute of Technology Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)

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**Training workshop on GIS and Remote Sensing Applications in Agricultural Meteorology for the (SADC)**

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