Include non-raster object in overlay function in R

Include non-raster object in overlay function in R

I have two raster objects and I would like to calculate values of the first conditional on values in the second. I am currently using the overlay function, but unfortunately I can't seem to pass non raster objects into the function.

As a toy example, r1 includes the values, r2 includes the conditions, and depending on the value of r2, I want to multiply r1 by a value (either .25, .5 or .75). (I know in this example I could just replace 1 with .25, etc. but I just created this as an example).

r1 = raster(nrow=5,ncol=5) r2 = raster(nrow=5, ncol=5) r1[] = runif(length(r1)) r2[] = round(runif(ncell(r1),min=1,max=3)) f_calcIt = function(a,b){ z = rep(NA,length(a)) i = which(b == 1) z[i] = a[i] * .25 i = which(b == 2) z[i] = a[i] * .50 i = which( == 3) z[i] = a[i] * .75 return(z) }b out = overlay(r1,r2, fun = f_calcIt)

This works, but I would like to do is include the scalars (0.25,0.50,.075) that are currently hardcoded in the function as a vector, and import into the function. For example,

d = c(.25,.50,.75) f_calcIt = function(a,b, d){ z = rep(NA,length(a)) i = which(b == 1) z[i] = a[i] * d[1] i = which(b == 2) z[i] = a[i] * d[2] i = which(b == 3) z[i] = a[i] * d[3] return(z) }

However, the use of this function returns an error that the formula is not vectorized.

Outside of creating a mirror raster with the scalar values in every cell, is there a way to accomplish this using overlay?

The reason that I would like to do so, (and assume others would also), is because it is not always convenient to hardcode values into function, especially if you want to reuse the functions.

You're trying to do two things at once: reclassify the values ofr2and then multiply those byr1. Instead, do them separately:

d <- 1:3/4 out = overlay(r1, calc(r2, fun=function(i) d[i]), fun="*")

It is, of course, your responsibility to ensure that the values ofr2are all valid indexes into arrayd.

whuber showed the way, but here's another way to get there (using the functions that match the operations he suggests)

d <- c(.25,.50,.75) m <- cbind(1:3, d) r3 <- reclassify(r2, m) out <- r1 * r3

Include non-raster object in overlay function in R - Geographic Information Systems

terrainr: Landscape Visualization in R and Unity

terrainr makes it easy to retrieve elevation and base map image tiles for areas of interest within the United States from the National Map family of APIs, and then process that data into larger, joined images or crop it into tiles that can be imported into the Unity 3D rendering engine.

There are three main utilities provided by terrainr. First, users are able to download data from the National Map via the get_tiles function, downloading data tiles for the area represented by an sf or Raster object:

Once downloaded, these images are in standard GeoTIFF or PNG formats and can be used as expected with other utilities:

Secondly, terrainr provides functions for manipulating these files, editing downloaded images to create new base map tiles:

Finally, terrainr helps you visualize this data, both natively in R via the new geom_spatial_rgb geom:

As well as with the Unity 3D rendering engine, allowing you to fly or walk through your downloaded data sets in 3D and VR:

The more time intensive processing steps can all be monitored via the progressr package, so you’ll be more confident that your computer is still churning along and not just stalled out. For more information, check out the introductory vignette and the guide to importing your data into Unity!

The following datasets can currently be downloaded using get_tiles or hit_national_map_api :

    : The USGS 3D Elevation Program (3DEP) Bare Earth DEM. : National Agriculture Imagery Program (NAIP) and high resolution orthoimagery (HRO). : A comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gauges, and dams). : Major civil areas for the Nation, including States or Territories, counties (or equivalents), Federal and Native American areas, congressional districts, minor civil divisions, incorporated places (such as cities and towns), and unincorporated places. : The USGS Elevation Contours service. : Information about physical and cultural geographic features, geographic areas, and locational entities that are generally recognizable and locatable by name. : A comprehensive set of digital spatial data comprising a nationally seamless network of stream reaches, elevation-based catchment areas, flow surfaces, and value-added attributes. : The name, function, location, and other core information and characteristics of selected manmade facilities. : Roads, railroads, trails, airports, and other features associated with the transport of people or commerce. : Hydrologic Unit (HU) polygon boundaries for the United States, Puerto Rico, and the U.S. Virgin Islands.

(All descriptions above taken from the National Map API descriptions.)

You can install the development version of terrainr from GitHub with:

Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Include non-raster object in overlay function in R - Geographic Information Systems

Data Model Definition: an abstraction of real world entities and their relationships into structures that can be implemented with a computer language.

  1. Definition
  2. Requirements for a DBMS
  3. Terminology
  4. Entity-Relationship conceptual model
  5. Hierarchical logical model
  6. Network logical model
  7. Relational logical model
  8. Integration of DBMS with spatial data models
  1. Introduction
  2. Raster conceptual data model
  3. Vector conceptual data model
  4. Object-oriented data model
    : referencing of an entity to a coordinate system (i.e. UTM, state plane . etc).
  1. Data description:
    1. data as entities: geographic data often described in phenomenological concepts such as roads, towns, rives floodplains, eoctypes, soil associations, . etc. These concepts are often referred to as entities.
    2. entity hierarchy: Data is often hierarchical in form (i.e. country > state > county > village forest > deciduous/coniferous > upland/lowland)
    3. gradients between entities: separations between some entities are not always clear cut and there may be a transitional zone between entities (ecotones).
    4. Geographic data can be represented using three basic topological concepts, the point, the line and the area. Every geographical phenomena can in principle be represented by these three concepts plus a label or attribute that defines it.
    5. What is a map? "A map is a set of points, lines and areas that are defined by their spatial location with respect to a coordinate system and by their non-spatial attributes" (Burrough 1986). A map legend links the non-spatial attributes to the spatial attributes.
    1. a record is a data structure containing information about an entity that can be manipulated as a unit.
    2. a pointer is a memory address that references the start of data in the RAM
    1. simple lists: unstructured data, each item is placed at the end of a list, search time (n + 1)/2.
    2. ordered sequential files: additions placed in proper position (insertion). Binary search is possible reducing search time log2(n+1). Item found in set of 65,535 + 1 items in 16 tries.
    3. indexed files:
      : Rapid data retrieval according to key attribute (i.e. dictionary spelling. Key attribute + additional information). In direct files the data items themselves provides means of ordering (soil series name with index to location of each name beginning with a particular letter) : May have ordered soil profiles but may want info on soil depth, drainage, ph, texture or erosion. If the poorly drained soils need to be identified we must use a linear search unless we invert the file. Inverted files are initially ordered using a linear search. An example of an inverted file is a topic index in a book.
      1. limitations of simple structures: file modification is difficult, a new record is added to the end of a file, then the index is updated. Data can be accessed only via a key contained in the indexed file, while other types of information requires a sequential search

      These data structures provide very efficient access to information pertaining to a single entity. But we need more. We need to relate different entities.

      II. Data Models (Laurini and Thompson, 1992 and ANSI/X3/SPARC, 1978)

      As data management became more complex a framework was need to understand the transformation of real world systems and processes into structures that could be implemented in a computer.

      1. External model: provide the basis for understanding the real world (e.g. non-spatial: a set entities spatial: the world as a constantly varying surface the world as a discrete set of objects in space or as a set of thematic layers)

      2. Conceptual data model: provide the organizing principles that translates the external data models into functional descriptions of how data objects are related to one another (e.g. non-spatial: E-R model spatial: raster, vector, object representation).

      3. Logical data model: provide the explicit forms that the conceptual models can take and is the first step in computing (e.g. non-spatial: hierarchical, network, relational spatial: 2-d matrix, map file, location list, point dictionary, arc/nodes).

      4. Internal data model: low level data structures, records, pointers, etc.

      III. Data Base Management Systems (DBMS)

      1- Definition:

      Data Base Management Systems: A system used to organize, access, maintain and manipulate object or entity data. A DBMS controls input, output, storage and retrieval of entity data. Essential features of a data base are fast access and cross referencing of entities.

      2- Requirements for a DBMS

      A DBMS should provide:

      1. Data Independence: the data base can change with little or no impact on the user programs
      2. Data Sharing: must have coordinated simultaneous access. Concurrency control mechanism.
      3. Maintenance of Data Integrity: DBMS helps enforce certain consistency constraints (i.e. coordinate has both lat and long, # of seats sold on an airplane <= # seats on plane)
      4. Security: DBMS provides mechanism for security/authorization from disclosure/destruction of data.
      5. Centrality of Control: DB administrator to resolve conflicts and meet user requirements
      6. Reduce Application Development Time

      3- E-R data model: a conceptual data model in which information is represented by entities and relationships between entities

      a. entity - a distinguishable object in the real world (people, forest stand, watershed, . etc.)
      b. relationship - a correspondence or association between two or more entities.
      c. attributes - the properties which describe an entity.
      d. functionality - how many entities from one entity set can be associated with another set
      e. primary key - main key for entity identification, one record per indexed attribute.
      f. secondary key - may have multiple record occurrences per index attribute.

      + easy to update and expand.
      + easy data access for keys.
      + ideal for data that is inherently hierarchical.
      - poor access for associated attributes.
      - Restrictive paths.
      - one to many relationships

      + reduces redundancy.
      + more flexible paths to data.
      + very fast
      - pointers expensive and difficult to update when inserting and deleting.

      7- Relational data model: data stored as records known as tuples grouped together in two-dimensional tables known as relations. Whereas hierarchical structures rely on the hierarchy and networks depend on pointers to associate entities, the relational model uses data redundancy in the form of unique keys that identify records in each file. Simplifies data maintenance because data for an entity type is stored in simple tables. Relational joins are used to cross reference entities using a primary key in one table and a foreign key in another table. Thus, in order to perform relational joins there needs to be at least one column in common between tables being related.

      The relational model is design to reduce redundancy of data whenever possible. A set of rules called the normal forms were developed by Codd (1970) to guide this process.

      + structures very flexible.
      + boolean logic and math operations.
      + insert and delete easy.
      - often use sequential search unless previously sorted.

      8- Integration of DBMS with the spatial data models

      IV. Data Models for Spatial Data

      Data structures are complex for GIS because they must include information pertaining to entities with respect to: position, topological relationships, and attribute information. It is the topologic and spatial aspects of GIS that distinguish it from other types of data bases.

      1. Introduction: There are presently three types of representations for geographic data: raster vector, and objects.

        - set of cells on a grid that represents an entity (entity --> symbol/color --> cells). -an entity is represented by nodes and their connecting arc or line segment (entity --> points, lines or areas --> connectivity)
      1. object – an entity is represented by an object which has as one of its attributes spatial information.

      Definition: realization of the external model which sees the world as a continuously varying surface (field) through the use of 2-D Cartesian arrays forming sets of thematic layers. Space is discretized into a set of connected two dimensional units called a tessellation.

      1. Each overlay is a 2-D matrix of points carrying the value of a single attribute.
      2. Each point is represented by a vertical array in which each array position carries a value of the attribute associated with the overlay. - each mapping unit has the coordinates for cell in which it occurs (greater structure, many to one relationship).

      Vertical array not conducive to compact data coding because it references different entities in sequence and it lacks many to one relationship. The third structure references a set of points for a region (or mapping unit) and allows for compaction.

      + reduced storage.
      + area, perimeter, shape est.
      - overlay difficult.

      + reduce storage.
      - overlay difficult.

      + reduced storage.
      + U & I of regions easy.

      Definition:realization of the discrete model of real world using structures for storing and relating points, lines and polygons in sets of thematic layers.

        1. represents an entity as exact as possible.
        2. coordinate space continuous (not quantized like raster).
        3. Structured as a set of thematic layers
          1. Point entities: geographic entities that are positioned by a single x,y coordinate. (historic site, wells, rare flora. The data record consists for x,y - attribute.
          2. Line Entity: (rivers, roads, rail)
            1. all linear feature are made up of line segments.
            2. a simple line 2 (x,y) coordinates.
            3. an arc or chain or string is a set of n (x,y) coordinate pairs that describe a continuous line. The shorter the line segments the closer the chain will approximate a continuous curve. Data record n(x,y).
            4. a line network gives information about connectivity between line segments in the form of pointers or relations contained in the data structure. Often build into nodes pointers to define connections and angles indicating orientation of connections (fully defines topology).

            3. Area Entity: data structures for storing regions. Data types, land cover, soils, geology, land tenure, census tract, etc.

              1. Cartographic spaghetti or "connect the dots". Early development in automated cartography, a substitute for mechanical drawing. Numerical storage, spatial structure evident only after plotting, not in file.
                • describe each entity by specifying coordinates around its perimeter.
                • shared lines between polygons.
                • polygon sliver problems.
                • no topology (neighbor and island problems).
                • error checking a problem.
                • unique points for entire file, no sharing of lines as in location lists (eliminate sliver problem) but still has other problems.
                • expensive searches to construct polygons.

                d. Dime Files (Dual Independent Mapping and Encoding)

                  • designed to represent points lines and areas that form a city though a complete representation of network of streets and other linear features.
                  • allowed for topologically based verification.
                  • no systems of directories linking segments together (maintenance problem).
                    • same topological principles as the DIME system.
                    • DIME defined by line segments, chains based on records of uncrossed boundary lines (curved roads a problem for DIME).
                    • chains or boundaries serve the topological function of connecting two end points called a node and separating two zones.
                    • points between zones cartographically not topologically required (generalization possible).
                    • solves problems discussed above (neighbor, dead ends, weird polygons).
                    • can treat data input and structure independently.

                    Definition: realization of the discrete model of real world using an object centered approach in which an object has both physical (attribute) and geometric characteristics. Different types of objects can interact because they are not confined to separate layers.

                    The biggest single difference between the object-oriented conceptual model and the vector-layered based conceptual model, for representing geographic information, is that in the object model, the real world object is the basis for abstraction, not its geometry. In other words, the objects not the geometric components of layers are the "units" for modeling and interactions

                    Overlay with Boolean Operators

                    Spatial intersection produces a new information layer with a variety of new spatial units. It is important to decide which newly created spatial units should be summarized and which must be recorded separately when applying this information to suitability analysis. For this task, Boolean algebra is used. It was established by the English mathematician and logician George Boole (1815 – 1864).

                    is based on the basics of binary logical operations. It forms a mathematical structure that is based only upon the values 1 (true) and 0 (false). In addition, Boolean algebra provides different links that can be "true" or "false" but never both. The Boolean operators that are used in GIS for linking two spatial selection criteria are AND, OR, XOR, and NOT.

                    AND Conjunction Results in "true" for all areas that meet both the first and the second criterion "Which areas are forested and steep?"
                    OR Disjunction Results in "true" for all areas that meet either the first or the second criterion, independent on the areas overlapping or not. In other words, at least one criterion has to be "true" "Which areas are forested or steep?"
                    XOR Exclusive disjunction Results in "true" for all areas that meet either the first or the second criterion but not both "Which areas are either forested or steep but not both at the same time?"
                    NOT Negation Results in "true" for all areas that meet the first criterion but not the second. "Which areas are forested but not steep?"
                    The four Boolean operators

                    In many GIS programs Boolean operators directly correspond to available functions and often carry a similar name. Functions like INTERSECT (AND), UNION (OR), and ERASE (NOT) are common functions in many GIS software. The last function is often called "cookie cutting" because the shape of the second criterion is "cut out" of the shape of the first – just like a cookie is cut from the dough. For suitability analysis, the actual Boolean overlay is usually preceded by a selection operation. In the case study, first the polygons with the attribute "forest" will be selected and translated to their own data layer with true / false information: "true" for "forest", and "false" for "no forest". The selection operation can also be carried out based on spatial operators. If a distance criterion is set (such as "at least 100 meters distance to the street"), a buffer function is applied before the overlay. The resulting binary information layers can then be combined with Boolean overlay.

                    The easiest way to explain Boolean operators is with the help of Venn diagrams. Each circle in the diagram stands for one criterion (criteria A and B). The sets are highlighted in blue if their corresponding Boolean expression results in "true". Choose a set in the Venn diagram and compare it with the corresponding Boolean expression. You can also proceed from the reverse: choose a Boolean expression and compare it with the selected set in the Venn diagram.

                    The following animation illustrates the Boolean overlay in the case study "Suitability analysis for a wolf habitat" in St. Gittal. Input data are slope and land use. Again, the vector and raster model are compared. First, choose the land use and slope categories you want to overlay (checkboxes in legend window). Second, choose the Boolean operator. The icon "Calculate" shows the result. An example: the operationalization of the criteria for potential habitats resulted in the land use "forest" and slope ">30%". Both are hard criteria and must be met (conjunction or intersection, AND). Select the appropriate checkboxes and the function AND. Calculate the Boolean overlay or the sought habitat respectively. Use the two animations to deepen your understanding of the Boolean overlay. Also, compare the vector and the raster solution.


                    The input raster whose values will be scaled from 0 to 1.

                    Specifies the algorithm used in fuzzification of the input raster.

                    The fuzzy classes are used to specify the type of membership.

                    • FuzzyGaussian(,)
                    • FuzzyLarge(,)
                    • FuzzyLinear(,)
                    • FuzzyMSLarge(,)
                    • FuzzyMSSmall(,)
                    • FuzzyNear(,)
                    • FuzzySmall(,)

                    Defining a hedge increases or decreases the fuzzy membership values which modify the meaning of a fuzzy set. Hedges are useful to help in controlling the criteria or important attributes.

                    • NONE —No hedge is applied. This is the default.
                    • SOMEWHAT —Known as dilation, defined as the square root of the fuzzy membership function. This hedge increases the fuzzy membership functions.
                    • VERY —Also known as concentration, defined as the fuzzy membership function squared. This hedge decreases the fuzzy membership functions.

                    Return Value

                    The output will be a floating-point raster with values ranging from 0 to 1.


                    The weighted sum table allows you to apply different weights to individual input rasters before they are summed together.

                    • Raster —The raster being weighted.
                    • Field —The field of the raster to use for weighting.
                    • Weight —The weight value by which to multiply the raster. It can be any positive or negative decimal value.

                    Return Value

                    The output weighted raster.

                    It will be of floating-point type.

                    The Weighted Sum tool overlays several rasters, multiplying each by their given weight and summing them together.

                    An Overlay class is used to define the table. The WSTable object is used to specify a Python list of input rasters and weight them accordingly.

                    Return Value

                    The output weighted raster.

                    It will be of floating-point type.

                    Code sample

                    This example creates a suitability raster for locating a ski resort by combining multiple rasters together and applying appropriate weight factors.

                    This example creates a suitability raster for locating a ski resort by combining multiple rasters together and applying appropriate weight factors.

                    Occasionally, the need arises for a parameter to accept multiple data types, often referred to as a composite data type. In a Python toolbox, composite data types are defined by assigning a list of data types to the parameter's datatype property. In the example below, a parameter is defined that accepts a Raster dataset or a Raster catalog.


                    The use of keywords for parameter data types was introduced at 10.1, service pack 1. Parameter descriptions can still be used but are not localized and cannot be used for different locales.

                    A dataset used for geocoding that stores the address attributes, associated indexes, and rules that define the process for translating nonspatial descriptions of places to spatial data.

                    A template on which to base the new address locator.

                    The cell size used by raster tools.

                    A data type that accepts any value.

                    A file that contains one map, its layout, and its associated layers, tables, charts, and reports.

                    An areal unit type and value, such as square meter or acre.

                    A vector data source mixed with feature types and symbology. The dataset is not usable for feature class-based queries or analysis.

                    The top-level node in the Catalog tree.

                    The cell size used by the ArcGIS Spatial Analyst extension .

                    Defines the two sides of a raster cell.

                    A reference to several children layers, including symbology and rendering properties.

                    Specifies the type of compression used for a raster.

                    A reference framework, such as the UTM system consisting of a set of points, lines, and/or surfaces, and a set of rules used to define the positions of points in two- and three-dimensional space.

                    Coordinate Systems Folder

                    A folder on disk storing coordinate systems.

                    A coverage dataset, a proprietary data model for storing geographic features as points, arcs, and polygons with associated feature attribute tables.

                    A coverage feature class, such as point, arc, node, route, route system, section, polygon, and region.

                    A dataset visible in ArcCatalog.

                    The database connection folder in ArcCatalog.

                    A collection of related data, usually grouped or stored together.

                    Attribute data stored in dBASE format.

                    Specifies a subset of nodes of a TIN to create a generalized version of that TIN.

                    An access path to a data storage device.

                    Any floating-point number stored as a double precision, 64-bit value.

                    Encrypted string for passwords.

                    The coordinate pairs that define the minimum bounding rectangle in which the data source falls.

                    The scale value range and increment value applied to inputs in a weighted overlay operation.

                    Specifies the coordinate pairs that define the minimum bounding rectangle (xmin, ymin and xmax, ymax) of a data source. All coordinates for the data source fall in this boundary.

                    An extract values parameter.

                    A collection of spatial data with the same shape type: point, multipoint, polyline, and polygon.

                    A collection of feature classes that share a common geographic area and the same spatial reference system.

                    A reference to a feature class, including symbology and rendering properties.

                    Interactive features that draw the features when the tool is run.

                    A column in a table that stores the values for a single attribute.

                    The details about a field in a FieldMap.

                    A collection of fields in one or more input tables.

                    Specifies a location on disk where data is stored.

                    A raster surface whose cell values are represented by a formula or constant.

                    Specifies the algorithm used in fuzzification of an input raster.

                    A collection of data with a common theme in a geodatabase.

                    A coarse-grained object that references a geodatabase.

                    A linear network represented by topologically connected edge and junction features. Feature connectivity is based on their geometric coincidence.

                    A reference to a geostatistical data source, including symbology and rendering properties.

                    Geostatistical Search Neighborhood

                    Defines the searching neighborhood parameters for a geostatistical layer.

                    Geostatistical Value Table

                    A collection of data sources and fields that define a geostatistical layer.

                    A collection of layers that appear and act as a single layer. Group layers make it easier to organize a map, assign advanced drawing order options, and share layers for use in other maps.

                    The relationship between the horizontal cost factor and the horizontal relative moving angle.

                    A data structure used to speed the search for records in geographic datasets and databases.

                    A syntax for defining and manipulating data in an INFO table.

                    A table in an INFO database.

                    A LAS dataset stores reference to one or more LAS files on disk, as well as to additional surface features. A LAS file is a binary file that stores airborne lidar data.

                    A layer that references a LAS dataset on disk. This layer can apply filters on lidar files and surface constraints referenced by a LAS dataset.

                    A reference to a data source, such as a shapefile, coverage, geodatabase feature class, or raster, including symbology and rendering properties.

                    A layer file stores a layer definition, including symbology and rendering properties.

                    A shape, straight or curved, defined by a connected series of unique x,y-coordinate pairs.

                    A linear unit type and value such as meter or feet.

                    A range of lowest and highest possible value for m-coordinates.

                    A collection of raster and image data that allows you to store, view, and query the data. It is a data model in the geodatabase used to manage a collection of raster datasets (images) stored as a catalog and viewed as a mosaicked image.

                    A layer that references a mosaic dataset.

                    The shape of the area around each cell used to calculate statistics.

                    Network Analyst Class FieldMap

                    Mapping between location properties in a Network Analyst layer (such as stops, facilities, and incidents) and a point feature class.

                    Network Analyst Hierarchy Settings

                    A hierarchy attribute that divides hierarchy values of a network dataset into three groups using two integers. The first integer sets the ending value of the first group the second number sets the beginning value of the third group.

                    A special group layer used to express and solve network routing problems. Each sublayer held in memory in a Network Analyst layer represents some aspect of the routing problem and the routing solution.

                    A collection of topologically connected network elements (edges, junctions, and turns), derived from network sources and associated with a collection of network attributes.

                    A reference to a network dataset, including symbology and rendering properties.

                    A parcel fabric is a dataset for the storage, maintenance, and editing of a continuous surface of connected parcels or parcel network.

                    A layer referencing a parcel fabric on disk. This layer works as a group layer organizing a set of related layers under a single layer.

                    A connected sequence of x,y-coordinate pairs, where the first and last coordinate pair are the same.

                    A file storing coordinate system information for spatial data.

                    Specifies if pyramids are built.

                    Specifies which surrounding points are used for interpolation.

                    Specifies the seed and the generator to use when creating random values.

                    A layer in a raster dataset.

                    Raster Calculator Expression

                    A raster calculator expression.

                    A collection of raster datasets defined in a table. Each table record defines an individual raster dataset in the catalog.

                    A reference to a raster catalog, including symbology and rendering properties.

                    A single dataset built from one or more rasters.

                    A reference to a raster, including symbology and rendering properties.

                    Specifies if raster statistics build.

                    Raster data is added to a mosaic dataset by specifying a raster type. The raster type identifies metadata, such as georeferencing, acquisition date, and sensor type, with a raster format.

                    Interactive table type in the table values when the tool is run.

                    The details about the relationship between objects in the geodatabase.

                    A table that defines how raster cell values are reclassified.

                    Route Measure Event Properties

                    Specifies the fields on a table that describe events measured by a linear reference route system.

                    A schematic dataset contains a collection of schematic diagram templates and schematic feature classes that share the same application domain, for example, water or electrical.

                    A schematic layer is a composite layer composed of feature layers based on the schematic feature classes associated with the template on which the schematic diagram is based.

                    Specifies the distance and direction representing two locations used to quantify autocorrelation.

                    Spatial data in a shapefile format.

                    The coordinate system used to store a spatial dataset, including the spatial domain.

                    A syntax for defining and manipulating data from a relational database.

                    A string that is masked by * characters.

                    The text is not encrypted when used in scripting.

                    A representation of tabular data for viewing and editing purposes, stored in memory or on disk.

                    A reference to a terrain, including symbology and rendering properties. It’s used to draw a terrain.

                    Data stored in ASCII format.

                    Specifies the width and the height of data stored in block.

                    Specifies the time periods used for calculating solar radiation at specific locations.

                    A vector data structure that partitions geographic space into contiguous, nonoverlapping triangles. The vertices of each triangle are sample data points with x-, y-, and z-values.

                    A reference to a TIN, including topological relationships, symbology, and rendering properties.

                    Features that are input to the interpolation.

                    A topology that defines and enforces data integrity rules for spatial data.

                    A reference to a topology, including symbology and rendering properties.

                    A collection of columns of values.

                    A data value that can contain any basic type: Boolean, date, double, long, and string.

                    Specifies the relationship between the vertical cost factor and the vertical, relative moving angle.

                    Spatial data stored in Vector Product Format.

                    Attribute data stored in Vector Product Format.

                    Web Coverage Service (WCS) is an open specification for sharing raster datasets on the web.

                    A table with data to combine multiple rasters by applying a common measurement scale of values to each raster, weighing each according to its importance.

                    Specifies data for overlaying several rasters, each multiplied by their given weight and summed.

                    Watch the video: R language tip: Create maps in R