Join by location, or spatial join, uses spatial associations between the layers involved to append fields from one layer to another. Spatial joins are different from attribute and relationship class joins in that they are not dynamic and require the results to be saved to a new output layer.
In essence, spatial querying is similar to attribute query by location but in this case we do not query the attribute data directly (as with a cursor). Instead, we query or find features that are located in certain way relative to other features.
1. What primary characteristic distinguishes a spatial join from an attribute join? A spatial join is similar to an attribute join, except that instead of using a common field to decide which rows in the table match, the locations of the spatial features are used.
An attribute join is used to append the fields of one table to another based on a common field. In order for an attribute join to work, the common field needs to be the same field type (e.g., string, numeric, etc.) and have identical formatting.
There are four types of spatial joins: outer join, inner join, left join, and right join. These spatial join types determine which features from both datasets are kept in the resulting output dataset.
Basically, we have only three types of joins: Inner join, Outer join, and Cross join.
The spatial attributes of the spatial object provide the information with respect to spatial locations, for example, latitude, elevation, longitude, and shape.
A spatial join matches rows from the Join Features values to the Target Features values based on their relative spatial locations. By default, all attributes of the join features are appended to attributes of the target features and copied to the output feature class.
The big difference between table joins and spatial joins is table joins are non-spatial, utilizing the values contained in the attribute table or non-spatial data table, while spatial joins utilize the actual features and their relationship with each other.
The spatial join tool inserts the columns from one feature table to another based on location or proximity. Let's say you have a set of land parcels. Each land parcel has a point inside of it. By running a spatial join, you can transfer the point table columns into the land parcel layer.
The main difference between attribute data and spatial data is that the attribute data describes the characteristics of a geographical feature while spatial data describes the absolute and relative location of geographic features.
Attribute data are the information linked to the geographic features (spatial data) that describe features. That is, attribute data are the “[n]on- graphic information associated with a point, line, or area elements in a GIS.”
What is the difference? Spatial data, also known as geospatial data, is a term used to describe any data related to or containing information about a specific location on the Earth's surface. Non-spatial data, on the other hand, is data that is independent of geographic location.
Attribute and topological query operations are also introduced. Spatial query refers to the process of retrieving a data subset from a map layer by working directly with the map features. In a spatial database, data are stored in attribute tables and feature/spatial tables.
While attribute queries select features by sorting through attribute records, spatial selection chooses features from the map in the user interface. In most cases, it selects features from one layer that fall within or touches an edge of polygon features in a second layer (or an interactively drawn graphic polygon).
Spatial data represents various aspects of geography as layers on a map. Attribute data stores information about those layers as rows and columns in a table. Layers can be queried, symbolized, and analyzed by their attributes to uncover geographic patterns and relationships.
Spatial joins are the bread-and-butter of spatial databases. They allow you to combine information from different tables by using spatial relationships as the join key. Much of what we think of as “standard GIS analysis” can be expressed as spatial joins.
Spatial Join, as the name implies, joins two feature classes to create a new table. It does not split line or polygon features based on the portion that is inside another polygon, unlike the Intersect tool. This means that you cannot create a tabulation table that has AREA and PERCENTAGE.
Spatial Relationships Types. Adjacency, contiguity, overlap, and proximity are the four ways of describing the relationship between two or more entities.
The Select Layer By Location tool has a Input Feature Layer parameter; the Spatial Join tool's equivalent parameter is Target Features. The Select Layer By Location tool has a Selecting Features parameter; the Spatial Join tool's equivalent parameter is Join Features.
To begin making a spatial join between a polygon layer and a point layer, right click on the polygon layer in the table of contents, and choose Joins and Relates>Join. The join dialogue opens. Make sure that in the dropdown at the top you have chosen “Join data from another layer based on spatial location.”
Spatial queries can split features when they cross each other. Spatial queries can only be executed on polygon feature classes. Spatial joins can use a more restricted set of spatial operators than spatial queries. Spatial joins combine the tables of the datasets being compared.
They have the data attributes of base (BINARY or DECIMAL), scale (FLOAT or FIXED), precision (significant digits and decimal-point placement), and mode (REAL or COMPLEX). Numeric character data is numeric data that is held in character form.
An attribute is a data value associated with a particular feature in a GIS layer—for example, the name associated with a particular street, the population of a particular city, or the median household income of a postal code area.
Attribute data is defined as a type of data that can be used to describe or quantify an object or entity. An example of attribute data is things like coluor, , yes/no, gender, etc. This type of data is typically used in conjunction with other forms of data to provide additional context and insights.