Part one.pdf

ArcToolbox and Model builder What is geoprocessing? The fundamental purpose of geoprocessing is to provide tools and a framework for performing analysis and managing your geographic data. The modelling and analysis capabilities geoprocessing provides make ArcGIS a complete geographic info...

ArcToolbox and Model builder What is geoprocessing? The fundamental purpose of geoprocessing is to provide tools and a framework for performing analysis and managing your geographic data. The modelling and analysis capabilities geoprocessing provides make ArcGIS a complete geographic information system. Geoprocessing provides a large suite of tools for performing GIS tasks that range from simple buffers and polygon overlays to complex regression analysis and image classification.  The tasks can be quite creative, using a sequence of operations to model and analyze complex spatial relationships. for example, calculating optimum paths through a transportation network, predicting the path of wildfire, analyzing and finding patterns in crime locations, predicting which areas are prone to landslides, or predicting flooding effects of a storm event. A typical geoprocessing tool performs an operation on an ArcGIS dataset (such as a feature class, raster, or table) and produces a new dataset as the result of the tool.  Geoprocessing allows you to chain together sequences of tools, feeding the output of one tool into another. You can use this ability to compose an infinite number of geoprocessing models (tool sequences) that help you automate your work and solve complex problems. You can share your work with others by packaging your workflow into an easily shared geoprocessing package. You can also create web services from your geoprocessing workflows. Working with geoprocessing in ArcCatalog There are three key concepts to geoprocessing in ArcGIS: 1. There is a large set of geoprocessing operators (called tools). These are organized and managed in ArcToolbox. 2. Individual tools can be chained together into a sequence most often called a process. You can program your own ideas quickly and efficiently. 3. You can use Python scripting and the ModelBuilder window to create geoprocessing models and scripts. Working with ArcToolbox ArcGIS includes a collection of geoprocessing tools that are organized into ArcToolbox. You can create your own tools by writing a script or building a model and organize these in the ArcToolbox. ArcToolbox contains a series of folders used to organize all the tools delivered with ArcGIS as well as any new tools that you create or add.  You can navigate ArcToolbox to open and work with geoprocessing tools by expanding the Toolboxes node in the Catalog Tree. What is ModelBuilder? ModelBuilder is a geoprocessing application in which you graphically create, edit, and manage models. Models are assembled as a logical sequence of tools where the outputs of one tool are fed into subsequent operations (i.e., tools) to produce a result. A user-friendly way to automate a series of tools Part of the ArcGIS geoprocessing framework ModelBuilder can run any tool in the ArcToolbox, including scripts, custom tools, and other models Supports GDBs, shapefiles, tables, coverages, rasters, CAD  Models are workflows that string together sequences of geoprocessing tools, providing the output of one tool to another tool as input. ModelBuilder can also be thought of as a visual programming language for building workflows. The benefits of ModelBuilder are summarized as follows: 1. ModelBuilder is an easy-to-use application for creating and running workflows containing a sequence of tools. 2. You can create your own tools with ModelBuilder. Tools you create with ModelBuilder can be used in Python scripting and other models. 3. ModelBuilder, along with scripting, is a way for you to integrate ArcGIS with other applications. Spatial Analysis What is Spatial Analysis in a narrow sense? Spatial analysis is a set of techniques for analyzing spatial data. The results of spatial analysis are dependent on the locations of the objects being analyzed. Software that implements spatial analysis techniques requires access to both the locations of objects and their attributes. Spatial analysis or spatial statistics includes any of the formal techniques which study entities using their topological, geometric, or geographic properties. What is Spatial Analysis in broad sense? Spatial Analysis includes revealing and clarifying processes, structures, etc., of spatial phenomena that occur on the Earth’s surface. Ultimately, it is designed to support spatial decision-making, and to serve as a tool for assisting with regional planning and the formulation of government policies, among other things. ✓ The world of GIS includes such terms as 1. spatial data manipulation, 2. spatial data analysis, 3. spatial statistical analysis, and 4. spatial modeling. Why Is Spatial Analysis Conducted with GIS? Why spatial analysis is conducted with GIS instead of statistical analysis packages such as SPSS or mathematical analysis tools (such as Mathematica)? The reason is simple. GIS manages planar data and attribute data in an integrated manner, which enables the spatial data to be manipulated in all directions. We can summarize its advantages as follows: 1.Calculating 2.Mining 3.Visualizing 4.Creating Data 5.Handling Relations 6.Understanding Processes 1.Calculating: measurements of distance, area, mass (capacity) etc. 2.Mining: the process of shifting out underlying patterns, causal relationships, etc., from enormous amounts of data 3.Visualizing: ability to turn data into images  Spatial Analyst toolsets Toolset Description The Conditional tools allow you to control the output values based on the conditions placed on Conditional the input values. The conditions that can be applied are of two types, those being either queries on the attributes or a condition based on the position of the conditional statement in a list. With the Density tools, you can calculate the density of input features within a neighborhood Density around each output raster cell. The Distance tools allow you to perform analysis that accounts for either straight-line Distance (Euclidean) or weighted distance. Distance can be weighted by a simple cost (friction) surface, or in ways that account for vertical and horizontal restrictions to movement. The Extraction tools allow you to extract a subset of cells from a raster by either the cells' Extraction attributes or their spatial location. You can also obtain the cell values for specific locations as an attribute in a point feature class or as a table. The generalization analysis tools are used to either clean up small erroneous data in the raster or Generalization generalize the data to get rid of unnecessary detail for a more general analysis. The Groundwater tools can be used to perform rudimentary advection-dispersion modeling of constituents in groundwater flow. The following topics provide background information on the Groundwater theoretical aspects of the tools as well as some examples of their implementation. The Groundwater tools can be applied individually or used in sequence to model and analyze groundwater flow.  The Hydrology tools are used to model the flow of water across a surface. Hydrology The Hydrology tools can be applied individually or used in sequence to create a stream network or delineate watersheds. The Interpolation tools create a continuous (or prediction) surface from sampled point values. The continuous surface representation of a raster dataset represents some measure, such as the Interpolation height, concentration, or magnitude (for example, elevation, acidity, or noise level). Surface interpolation tools make predictions from sample measurements for all locations in an output raster dataset, whether or not a measurement has been taken at the location. The local tools are those where the value at each cell location on the output raster is a function of the values from all the inputs at that location. Local With the local tools, you can combine the input rasters, calculate a statistic on them, or evaluate a criterion for each cell on the output raster based on the values of each cell from multiple input rasters. Map Algebra is a way to perform spatial analysis by creating expressions in an algebraic language. Map Algebra With the Raster Calculator tool, you can easily create and run Map Algebra expressions that output a raster dataset. The general Math tools apply a mathematical function to the input. These tools fall into several categories. The arithmetic tools perform basic mathematical operations, such as addition and Math (general) multiplication. There are tools that perform various types of exponentiation operations, which includes exponentials and logarithms in addition to the basic power operations. The remaining tools are used either for sign conversion or for conversion between integer and floating point data types. Math Bitwise The bitwise math tools compute on the binary representation of the input values. The Logical Math tools evaluate the values of the inputs and determine the output Math Logical values based on Boolean logic. The tools are grouped into four main categories: Boolean, Combinatorial, Logical, and Relational. Math Trigonometric Math tools perform various trigonometric calculations on the values in Trigonometric an input raster. Multivariate statistical analysis allows the exploration of relationships among many different types of attributes. There are two types of multivariate analysis available: Multivariate Classification (both Supervised and Unsupervised) and Principal Component Analysis (PCA). Neighborhood tools create output values for each cell location based on the location Neighborhood value and the values identified in a specified neighborhood. The neighborhood type can be either moving or search radius. Overlay analysis tools allow you to apply weights to several input layers, combine them into a single output, and subject to specifications of distribution and shape, Overlay identify preferred locations within that result. These tools are commonly used for suitability modeling. The Raster Creation tools generate new rasters in which the output values are based Raster Creation on a constant or a statistical distribution.  The Reclass tools provide a variety of methods that allow you to reclassify or change Reclass input cell values to alternative values. Segmentation With the Segmentation and Classification tools, you can prepare segmented rasters and to use in creating classified raster datasets. Classification The solar radiation analysis tools enable you to map and analyze the effects of the Solar Radiation sun over a geographic area for specific time periods. With the Surface tools, you can quantify and visualize a terrain landform represented Surface by a digital elevation model. The Zonal tools allow you to perform analysis where the output is a result of computations performed on all cells that belong to each input zone. A zone can be defined as being one single area of a particular value, but it can also be composed of Zonal multiple disconnected elements, or regions, all having the same value. Zones can be defined by raster or feature datasets. Rasters must be of integer type, and features must have an integer or string attribute field. Interpolation is a method of estimating unknown values using known values of neighboring locations. The Interpolation tools create a continuous (or prediction) surface from sampled point values. Visiting every location in a study area to measure the height, concentration, or magnitude of a phenomenon is usually difficult or expensive. Instead, you can measure the phenomenon at strategically dispersed sample locations, and predicted values can be assigned to all other locations. Input points can be either randomly or regularly spaced or based on a sampling scheme. The continuous surface representation of a raster dataset represents some measure, such as the height, concentration, or magnitude (for example, elevation, acidity, or noise level).  Surface interpolation tools make predictions from sample measurements for all locations in an output raster dataset, whether or not a measurement has been taken at the location. There are a variety of ways to derive a prediction for each location; each method is referred to as a model. With each model, there are different assumptions made of the data, and certain models are more applicable for specific data. for example, one model may account for local variation better than another. Each model produces predictions using different calculations. The interpolation tools are generally divided into deterministic and geostatistical methods. 1. The deterministic interpolation methods assign values to locations based on the surrounding measured values and on specified mathematical formulas that determine the smoothness of the resulting surface. The deterministic methods include IDW (inverse distance weighting), Natural Neighbor, Trend, and Spline. 2. The geostatistical methods are based on statistical models that include autocorrelation (the statistical relationship among the measured points). Because of this, geostatistical techniques not only have the capability of producing a prediction surface but also provide some measure of the certainty or accuracy of the predictions. Kriging is a geostatistical method of interpolation. Tool Description Interpolates a raster surface from points using an inverse distance IDW weighted (IDW) technique. Kriging Interpolates a raster surface from points using kriging. Interpolates a raster surface from points using a natural neighbor Natural Neighbor technique. Interpolates a raster surface from points using a two-dimensional Spline minimum curvature spline technique. The resulting smooth surface passes exactly through the input points. Interpolates a raster surface, using barriers, from points using a minimum Spline with Barriers curvature spline technique. The barriers are entered as either polygon or polyline features. Interpolates a hydrologically correct raster surface from point, line, and Topo to Raster polygon data. Interpolates a hydrologically correct raster surface from point, line, and Topo to Raster by File polygon data using parameters specified in a file. Trend Interpolates a raster surface from points using a trend technique IDW The IDW (Inverse Distance Weighted) tool uses a method of interpolation that estimates cell values by averaging the values of sample data points in the neighborhood of each processing cell. The closer a point is to the center of the cell being estimated, the more influence, or weight, it has in the averaging process. The best results from IDW are obtained when sampling is sufficiently dense with regard to the local variation you are attempting to simulate. If the sampling of input points is sparse or uneven, the results may not sufficiently represent the desired surface (Watson and Philip 1985). Kriging Kriging is an advanced geostatistical procedure that generates an estimated surface from a scattered set of points with z-values. More so than other interpolation methods, a thorough investigation of the spatial behavior of the phenomenon represented by the z-values should be done before you select the best estimation method for generating the output surface. Kriging assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. Kriging fits a mathematical function to a specified number of points, or all points within a specified radius, to determine the output value for each location. Kriging is a multistep process; it includes exploratory statistical analysis of the data, variogram modeling, creating the surface, and (optionally) exploring a variance surface. Kriging is most appropriate when you know there is a spatially correlated distance or directional bias in the data. It is often used in soil science and geology Kriging methods There are two kriging methods: Ordinary and Universal. Ordinary Kriging is the most general and widely used of the kriging methods and is the default. It assumes the constant mean is unknown. This is a reasonable assumption unless there is a scientific reason to reject it. Universal Kriging assumes that there is an overriding trend in the data—for example, a prevailing wind—and it can be modeled by a deterministic function, a polynomial. This polynomial is subtracted from the original measured points, and the autocorrelation is modeled from the random errors. Once the model is fit to the random errors and before making a prediction, the polynomial is added back to the predictions to give meaningful results. Universal Kriging should only be used when you know there is a trend in your data, and you can give a scientific justification to describe it. Spline The Spline tool uses an interpolation method that estimates values using a mathematical function that minimizes overall surface curvature, resulting in a smooth surface that passes exactly through the input points. The Regularized option of Spline type usually produces smoother surfaces than those created with the Tension option. With the Regularized option, higher values used for the weight parameter produce smoother surfaces. The values entered for this parameter must be equal to or greater than zero. Typical values used are 0, 0.001, 0.01, 0.1, and 0.5. The Weight is the square of the parameter referred to in the literature as tau (t).  With the Tension option, higher values entered for the weight parameter result in somewhat coarser surfaces, but surfaces that closely conform to the control points. The values entered must be equal to or greater than zero. Typical values are 0, 1, 5, and 10. The Weight is the square of the parameter referred to in the literature as phi The greater the value of Number of Points, the smoother the surface of the output raster. Some input datasets may have several points with the same x,y coordinates. If the values of the points at the common location are the same, they are considered duplicates and have no effect on the output. If the values are different, they are considered coincident points. Natural neighbor Natural Neighbor interpolation finds the closest subset of input samples to a query point and applies weights to them based on proportionate areas to interpolate a value (Sibson, 1981). It is also known as Sibson or "area-stealing" interpolation. Proximity toolset The Proximity toolset contains tools that are used to determine the proximity of features within one or more feature classes or between two feature classes. These tools can identify features that are closest to one another or calculate the distances between or around them. Tool Description Creates buffer polygons around input features to a specified Buffer distance. Creates Thiessen polygons from point features. CreateThiessen Each Thiessen polygon contains only a single point input Polygons feature. Any location within a Thiessen polygon is closer to its associated point than to any other point input feature. Calculates distances and other proximity information between features in one or more feature class or layer. Unlike the Near Generate Near Table tool, which modifies the input, Generate Near Table writes results to a new stand-alone table and supports finding more than one near feature.  Creates buffer polygons around input features to a specified distance. Graphic Buffer A number of cartographic shapes are available for buffer ends (caps) and corners (joins) when the buffer is generated around the feature. Creates multiple buffers at specified distances around the input Multiple Ring features. These buffers can optionally be merged and dissolved using Buffer the buffer distance values to create non-overlapping buffers. Calculates distance and additional proximity information between the Near input features and the closest feature in another layer or feature class. Determines the distances from input point features to all points in the Point Distance near features within a specified search radius. Polygon Creates a table with statistics based on polygon contiguity (overlaps, Neighbors coincident edges, or nodes).  Buffering is the process of creating an output polygon layer containing a zone (or zones) of a specified width around an input point, line, or polygon feature. Creates buffer polygons around input features to a specified distance. Hydrological analysis The area upon which water falls and the network through which it travels to an outlet are referred to as a drainage system. The flow of water through a drainage system is only a subset of what is commonly referred to as the hydrologic cycle, which also includes precipitation, evapotranspiration, and groundwater. A drainage basin is an area that drains water and other substances to a common outlet. Other common terms for a drainage basin are watershed, basin, catchment, or contributing area. This area is normally defined as the total area flowing to a given outlet, or pour point. Hydrological analysis A pour point is the point at which water flows out of an area. This is usually the lowest point along the boundary of the drainage basin. The boundary between two basins is referred to as a drainage divide or watershed boundary. Hydrological analysis The network through which water travels to the outlet can be visualized as a tree, with the base of the tree being the outlet. The branches of the tree are stream channels. The intersection of two stream channels is referred to as a node or junction. The sections of a stream channel connecting two successive junctions or a junction and the outlet are referred to as stream links. Hydrological analysis used to model the flow of water across a surface. to know where the water came from and where it is going. Basin: creates a raster delineating all drainage basins. Fill: fills sinks in a surface raster to remove small imperfections in the data. Flow accumulation: creates a raster of accumulated flow into each cell. A weight factor can optionally be applied. Flow direction: creates a raster of flow direction from each cell to its downslope neighbor, or neighbors, using D8, Multiple Flow Direction (MFD) or D-Infinity (DINF) methods. Hydrological analysis Flow distance: Computes, for each cell, the horizontal or vertical component of minimum downslope distance, following the flow path(s), to cell(s) on a stream into which they flow. Flow length: Calculates the upstream or downstream distance, or weighted distance, along the flow path for each cell. Sink: Creates a raster identifying all sinks or areas of internal drainage. Snap pour point: Snaps pour points to the cell of highest flow accumulation within a specified distance. Hydrological analysis Stream link: Assigns unique values to sections of a raster linear network between intersections. Stream order: Assigns a numeric order to segments of a raster representing branches of a linear network. Stream to feature: Converts a raster representing a linear network to features representing the linear network. Watershed: Determines the contributing area above a set of cells in a raster.