In government, urban planners work to optimize the best route for a new interstate, which requires information garnered from layers of data, including environment the highway should avoid remediation, wetlands, etc.
Alone, each of these layers provide an insight. Together, they provide the blueprint from which we can construct a multi-million-dollar highway.
The future consists of high quality data, optimizing storage and developing the right analytical tools that give managers a means to craft better decisions. For example, insurance companies must comprehend the risks and perils of certain geographic regions so they can effectively save money through more efficient underwriting.
Today, more variables that impact the pricing of policies can now be modeled because of advanced computing resources, precise location-based information, and authoritative data.
Telecommunications companies are always seeking to provide better coverage, especially when planning the rollout of a new product or service, such as 5G. To determine the needs of the local market, wireless providers are looking for population centers with multiple transmitters.
The more transmitters there are in a given area, the shorter the wavelength, and higher the frequency—and the better the signal. But in addition to transmitters, the telecom companies need to determine the location and extent of the target market. Here, layering of data make a lot of sense. Because there are several steps involved in completely loading a data layer it can become very confusing if many layers are loaded at once.
The proper identification of layers prior to starting data input is critical. The identification of data layers is often achieved through a user needs analysis. The user needs analysis performs several functions including:. Often a user needs assessment will include a review of existing operations, e. The cost-benefit process is well established in conventional data processing and serves as the mechanism to justify acquisition of hardware and software.
It defines and compares costs against potential benefits. Most institutions will require this step before a GIS acquisition can be undertaken. Most GIS projects integrate data layers to create derived themes or layers that represent the result of some calculation or geographic model, e. Derived data layers are completely dependant on the aim of the project. Each data layer would be input individually and topologically integrated to create combined data layers.
Based on the data model, e. It is important to note that in vector based GIS software the topological structure defined can only be traversed by means of unique labels to every feature. The proprietary organization of data layers in a horizontal fashion within a GIS is known as spatial indexing. Spatial indexing is the method utilized by the software to store and retrieve spatial data. A variety of different strategies exist for speeding up the spatial feature retrieval process within a GIS software product.
Most involve the partitioning of the geographic area into manageable subsets or tiles. These tiles are then indexed mathematically, e. Spatial indexing is analygous to the definition of map sheets, except that specific indexing techniques are used to access data across map sheet tile boundaries. Layers are reusable and flexible. For example, you can use the same imagery layer in every map you create.
If the location of the data changes, however, the data source path must be updated. Layers typically comprise vector feature or raster data. The type of layer depends on the type of data you have, its underlying structure, and some other variables. To determine the layer type, click it in the Contents pane. The layer type contextual tab set appears in the ribbon. Feature layers represent geographic objects as vectors and can be symbolized in a variety of ways depending on their attribution.
Feature layer data references feature classes , which are stored in geodatabases. Because they comprise vectors, the features can be symbolized with the same symbol or with unique symbols based on values from one or more attribute fields.
In the case of quantitative data, commonly used for thematic mapping, a layer can be represented with defined classification ranges.
Further symbology options include representing features as proportional symbols , charts , or dot density maps. There are other types of feature layers, such as stream layers , map notes layers , and bin-enabled feature layers , that aggregate features and map the results based on their attributes.
Raster layers reference rasters or images as their data source. They can be visualized as a single raster dataset or as a mosaic layer that references a mosaic dataset that manages large collections of raster data.
A variety of display types are available for visualizing raster data, depending on the raster band count, the presence of a color map, and whether the raster represents unique value data. As with vector layers, rasters can be classified using a variety of standard classification techniques. A variety of image analysis capabilities are available for performing visual analysis of rasters, including processing functions. If the layer has a three-dimensional aspect, it may be used to create a scene layer.
Scene layers are cached to optimize the display of 3D data, and the cache is created as part of a scene layer package. For example, a building scene layer may reference data from feature classes to render the model of a building. Depending on the type of data, scene layers can be queried, symbolized, labeled and edited. Maps and scenes can contain layers that reference map , feature , tile , vector tile and OGC services.
The majority of service types either have prerendered content or render the content on the server side. Map service layers can be enabled to support dynamic server-side updates. Feature services allow vector features to be drawn on the client side with the full set of ArcGIS symbology.
A stream layer references real-time observations and draws the changes. Most layers are one of the above types. Because GIS data varies in organization and complexity, there are many more types. For example, a group layer refers to a collection of layers. Some other common types of layers include: Query layer —Uses SQL queries to access and reference spatial and nonspatial database tables Selection layer —References a subset of features from an existing layer Subtype layer —Symbolizes a subtype in a feature class or feature service, as part of a subtype group layer Voxel layer —A type of 3D grid-based layer for displaying spatiotemporal data Graphics layer —Represents geographic objects but does not reference a dataset.
Layers are managed from the Contents pane. You can turn layers on and off from there and re-order them as needed. Maps draw layers in 2D. To move a layer from one category to another, click the layer name in the Contents pane and drag it into the other category. Explore layers in a map by panning around them to see different areas and zooming in and out to see those areas at different scales.
To learn how to navigate a map, see Keyboard shortcuts for navigation. If you're comparing two or more maps, you can link two or more open maps together so they stay in sync as you navigate.
0コメント