Glossary#

Application Programming Interface#: An application programming interface or API is a set of protocols and tools that enable pieces of software to communicate and exchange information. For example, the Nominatim service has an API for accessing its geocoding service.
API#: See Application Programming Interface.
Argument#: The value passed to a function when it is called. Similar to a parameter.
Binary predicate#: See Spatial predicate.
Collection#: A group of data types known as containers, where multiple values can be stored together. The built-in container data types in Python are dictionary, list, set, and tuple.
Computer#: We use the definition of a computer given by Zelle, 2017: “A machine that stores and manipulates information under the control of a changeable program.”
Coordinate Reference System#: A coordinate reference system (CRS) described how the coordinates or geometries are related to the places on Earth. It typically includes a set of geographic or projected coordinates and a mathematical model that describes the shape of the Earth and the relationship between the coordinates and their positions on the Earth’s surface. A CRS is used to locate positions accurately and to enable the exchange of geographic data between different systems and applications.
Coordinate transformation#: See Map reprojection.
DataFrame#: In pandas library, a DataFrame is a two-dimensional, tabular data structure with labeled rows and columns, similar to an Excel spreadsheet or SQL table. Each column can store data of different types (e.g., integers, floats, strings), and the DataFrame provides methods for data manipulation, including filtering, aggregation, and merging. It is one of the core data structures in pandas, widely used for data analysis and manipulation in Python.
Data model#: A data model is an conceptual (abstract) model that shows how elements of data are organized and how they relate to one another in a standardized manner and how the data relate to properties of real-world entities. Examples of data models are e.g. vector data model consisting of points, lines and areas; and raster data model constituted of a grid-like structure that hold the values for each grid cell.
Data type#: An attribute defining the characteristics of a value in a program. For example, type int is an integer (whole number).
DateOffsets#: A specific pandas object that represents a duration of time following calendar duration rules, such as a week (“W”).
DatetimeIndex#: An immutable array of datetime64 data that is specified as the index of the DataFrame. Can be used for indexing and grouping data based on time.
DE-9IM#: See Dimensionally Extended 9-Intersection Model.
Dependency#: Python packages are often linked to other Python libraries. These other packages (i.e. dependencies) are typically needed to be installed for a given Python package to work.
Dictionary#: A built-in Python data structure comprising one or more unique “keys” and associated “values”. Dictionary keys can be used to access associated values within a dictionary, and keys are typically numbers or character strings. Python dictionaries are enclosed in curly braces ({ }) and dictionary items are listed in the form key: value separated by commas.
Dimensionally Extended 9-Intersection Model #: Dimensionally Extended 9-Intersection Model (DE-9IM) is a fundamental framework in GIS used for describing and analyzing spatial relationships between geometric objects. DE-9IM provides a matrix-based approach where the rows and columns represent the interior, boundary, and exterior of two geometric shapes being compared. By examining the intersections of these parts, a detailed characterization of their spatial relationship can be achieved, including spatial predicates such as “touches”, “overlaps”, and “contains”.
Docstring#: A text string used to document a section of code. Docstrings are frequently used for functions to describe what the function does as well as providing information about input parameters and function outputs. You are encouraged to create docstrings when making functions as they can be used with the Python help function to show users how functions work.
Decimal degrees#: A decimal degree is a method of expressing latitude and longitude geographic coordinates as decimal fractions instead of degrees, minutes, and seconds. It represents the angle between a point on the earth’s surface and the equator or prime meridian, respectively, in units of decimal degrees. Decimal degrees provide a more convenient representation of geographic coordinates and make it easier to perform calculations with them.
Function#: A reusable piece of code that performs a single action.
Geocoding#: The process of converting addresses to coordinates / points, or vice versa (called reverse-geocoding). Also see Georeferencing.
GeoDataFrame#: GeoDataFrame in geopandas library is similar to pandas DataFrame but designed to handle geospatial data. Each row in a GeoDataFrame represents a geometric object (e.g., point, line, polygon) with associated attributes. GeoDataFrame bundles various methods that support spatial operations like geometric calculations and spatial joins, making it essential for geospatial analysis in Python.
Geographic coordinate conversion#: See Map reprojection.
Georeferencing#: Attaching information about a location to a piece of information is commonly referred as georeferencing, geolocating or geocoding. For example a postal address can be used to specify a location of a place with relatively high spatial accuracy at a level of door/mailbox.
GeoSeries#: GeoSeries is used to store geospatial data, where each element is a geometric object like a point, line, or polygon. It extends the pandas Series by supporting spatial operations, such as geometric transformations and spatial queries. A GeoSeries is often used to represent the geometry column in a GeoDataFrame, making it a fundamental building block for geospatial analysis in Python.
IDE#: See Integrated Development Environment.
Immutable#: A data type that can be modified after being defined. In reality, things are a bit more complicated, but this is sufficient for our purposes. Opposite of Mutable.
Index#: A number indicating the location of a specific value stored in Python lists or tuples. The first index value of list is always 0.
Inner join#: The inner join results in a DataFrame that contains only the rows with matching keys (or binary/spatial predicate) in both original (Geo)DataFrames, excluding all rows in either (Geo)DataFrame that do not have a match in the other. The match can be based on a common key that can be found in both DataFrames (as in table join) or on a spatial predicate in case the data is merged based on the spatial relationship between two spatial data layers.
Integrated Development Environment#: An integrated development environment or IDE is a software program or package that provides a set of tools for writing, testing, and debugging software in a convenient, practical interface.
Interpreter#: An interpreter is a computer program that is used to execute program instructions written in Python (or other languages). The interpreter reads your statements of code and based on these instructions actually does the work that has been assigned to it.
Jupyter Notebook#: A web application that allows users to combine rich-formtted text with code cells in an interactive document. Jupyter Notebooks can contain nicely formatted text, equations, images, interactive visualizations, and more. More information can be found at https://jupyter.org/.
KD-Tree#: A KD-Tree, or K-dimensional tree, is a space-partitioning data structure for organizing points in a k-dimensional space. KD-Trees are useful for making specific search tasks faster and more efficient, such as nearest neighbor search. The structure recursively divides the space into two half-spaces at each level, using one dimension at each step. This division is typically done by selecting a median value along one dimension to split the dataset, creating a binary tree. KD-Trees enable efficient querying of the space, such as finding points within a given range or nearest to a specific point, by significantly reducing the number of comparisons needed to locate them.
K-Nearest Neighbor search#: K-Nearest Neighbor (KNN) search is a type of algorithm used to find the “k” closest points (or neighbors) to a given query point in a dataset. In the context of spatial data, KNN search can identify the nearest geographical features based on their spatial coordinates. The algorithm calculates distances between the query point and all points in the dataset, then selects the “k” smallest distances to determine the nearest neighbors.
Left outer join#: A left outer join includes all the rows from the left (Geo)DataFrame and those rows from the right (Geo)DataFrame that have a matching key in the table, or that intersect or match based on the specified spatial relationship (e.g., intersects, contains, within). If there is no matching row in the right (Geo)DataFrame for a row in the left (Geo)DataFrame, the result will still include the row from the left (Geo)DataFrame, but with missing values (NaNs) in the columns from the right (Geo)DataFrame.
Library#: A group of related modules. See definition of a module.
List#: A data type in Python that can be used to store collections of values. Values in Python lists can be added, removed, or modified, and the list items do not need to be the same data types. Python lists are enclosed in square brackets ([ ]) and list items are separated by commas.
Loop#: A programming construct that allows a section of code to be repeated a finite number of times or until a given condition is met.
Map projection#: A map projection is a mathematical method to draw a graphical representation of the Earth’s surface on a flat surface, i.e. a map.
Map reprojection#: Map reprojection is a process of converting coordinates described in one coordinate reference system (CRS) to another. The transformation between coordinate systems involves both translation and rotation, and requires knowledge of the shape and size of the earth, as well as its orientation in space.
Markdown#: A lightweight markup language used to convert plain text input to rich-formatted output. Markdown can, for example, be used to create simple documentation with different heading levels, text in bold and/or italics, text in lists, or documentation that includes hyperlinks. More information can be found at https://en.wikipedia.org/wiki/Markdown.
Metadata#: Metadata refers to data that provides information about other data. It often describes characteristics of the data, such as its content, quality, format, and other relevant characteristics. For example, the metadata of a satellite image may include information about the image’s resolution, file size, coordinate reference system, and the date it was taken.
Method#: A function that is associated with an instance of a specific Python data type. Methods can be accessed by typing the variable name of the instance, a period, and the function name.
Module#: A file containing Python definitions and statements. Module files have the .py file extension.
Mutable#: A data type that can be modified after being defined. In reality, things are a bit more complicated, but this is sufficient for our purposes. Opposite of Immutable.
Namespace#: A collection of names and information about the objects to which the names refer. The objects could be variables or methods. There are four namespaces in Python (sorted here in order of scope from broadest to narrowest): built-in, global, enclosing, and local.
Optional parameter#: A function parameter that does not need to be provided when calling the function in order to use it. Optional parameters will use default function values that are provided in the function definition in such cases.
Parameter#: A variable listed within the parentheses of a function definition. Similar to an argument.
Program#: A detailed list of step-by-step instructions that tell the computer exactly what to do.
Programming language#: A set of exact and unambiguous instructions that can be understood by the computer.
Radius query#: A radius query is a type of spatial query that retrieves all points within a specified distance (radius) from a given query point. The query is performed typically on a KD-Tree to efficiently find points that fall within a given distance from the query point. KD-Tree supports only Point objects, i.e. it cannot be used to search other geometric types, such as LineStrings or Polygons.
Required parameter#: A function parameter that must be defined when calling the function in order to use it. Required parameters do not have default function values given in the function definition. Also known as positional parameters.
Right outer join#: Right outer join includes all the rows from the right (Geo)DataFrame and those rows from the left (Geo)DataFrame that have a matching key in the table, or that intersect or match based on the specified spatial relationship (e.g., intersects, contains, within). If there is no matching row in the left (Geo)DataFrame for a row in the right (Geo)DataFrame, the result will still include the row from the right (Geo)DataFrame, but with missing values (NaNs) in the columns from the left (Geo)DataFrame.
R-tree#: An R-tree is a type of data structure used as a spatial index to efficiently query spatial objects. It organizes spatial data by grouping nearby objects and representing these groups with their minimum bounding rectangle. This hierarchical structure allows to query spatial data efficiently by eliminating groups of objects that do not intersect with the query area. R-trees are widely used in geographic information systems (GIS), databases, and for various spatial search and optimization tasks, enabling fast access and retrieval of spatial data based on their location and geometric properties.
Scope#: The region in which a name defined in a program has meaning. Python will search for names within the scope of namespaces; first in the local, then enclosing, then global, and finally in built-in.
Script#: A Python script is a collection of commands in a file that can be executed like a program.
Semantics#: The exact meaning of a component in a programming language, such as a statement or a function. For example, the len() function in Python is used to determine the length of a data structure that is defined in memory.
Series#: In pandas library, a Series is a one-dimensional array-like data structure that holds a sequence of values, each associated with a unique index. It can store data of any type, such as integers, floats, or strings. A Series is essentially a single column of data and can be used as a building block for a DataFrame. It supports various operations for data manipulation and analysis, making it a fundamental component of pandas.
Software#: Another name for a program.
Spatial index#: A spatial index is a data structure designed to efficiently query spatial data, such as Points, LineStrings and Polygons within a multidimensional space. It optimizes the storage of spatial objects, enabling fast searches based on their geographic location and spatial relationships. Spatial indexes are crucial in geographic information systems (GIS) and spatial database management, where rapid access to spatial data is essential for tasks such as proximity searches, map rendering and spatial analysis. Common types of spatial indexes include R-trees, quad-trees, and k-d trees, each with its own method for partitioning space and organizing spatial data for efficient querying.
Spatial join#: A spatial join is a method used to combine spatial data based on the locations and relationships of the features in two separate geospatial datasets. It merges the two datasets based on their spatial relations, e.g. by joining information from all points in a given dataset that are within given polygons present in another dataset.
Spatial queries#: A spatial query is a process in GIS commonly used e.g. to select spatial features or properties based on the spatial relationships between geospatial data layers or geometries. Spatial queries are used for answering topology related questions on the basis of geometric information only, i.e., the spatial position and extent of the geometric objects involved.
Spatial resolution#: The spatial resolution of a raster refers typically to the size of the cells in a raster dataset. It can also mean the ratio of screen pixels to image pixels at the current map scale.
Spatial predicate#: Spatial predicate is an output from DE-9IM model that describe the spatial relationship between two geometric objects. Example spatial predicates are within, contains, touches and intersect.
Spatio-temporal data model#: A data model that incorporates time (t) as an additional dimension to the geographical dimension (x, y).
Subplots#: The term used in Matplotlib to refer to individual plots when more than one plot is part of a single figure.
Syntax#: The precise form of a component in a programming language. For example, the print function in Python expects the syntax print('hello') in order to have the word hello displayed on the screen.
Topological spatial relations#: Topological spatial relations describe how two or more geometric objects relate to each other concerning their position and boundaries. Topological spatial relations can be exemplified by relationships such as contains, touches and intersects. In GIS, these kind of topological relations play a crucial role as they enable queries that are less concerned with the exact coordinates or shapes of geographic entities but more focused on their relative arrangements and positions.
Tuple#: A tuple is a Python data structure that consists of a number of values separated by commas. Coordinate pairs are often represented as a tuple, such as: (60.192059, 24.945831). Tuples belong to sequence data types in Python. Other sequence data types are lists and ranges. Tuples have many similarities with lists and ranges, but they are often used for different purposes. The main difference between tuples and lists is that tuples are immutable, which means that the contents of a tuple cannot be altered (while lists are mutable; you can, for example, add and remove values from lists).
Type conversion#: Changing the data type of a Python object. For example, float(5).
Unary union#: The unary union operation takes multiple geometries and merges them into a single geometry. This is useful when you have a collection of shapes and you want to treat them as a single entity for analysis or visualization.
Variable#: A way of storing values in the memory of the computer using specific names that you define.
Virtual environment#: A virtual environment is a Python programming environment which works in a way that the Python interpreter, libraries and scripts installed into it are isolated from the ones installed in other virtual environments, as well as from (possible) system Python, i.e., one which is installed as part of your operating system.
Well-known binary#: Well-known binary (WKB) is a format for representing vector geometry objects in compressed binary format which is useful for computer processing. The human-readable equivalent for WKB is Well-known text format. WKB can represent various geometric objects: Point, MultiPoint, LineString, MultiLineString, Polygon, MultiPolygon, Triangle, PolyhedralSurface, TIN (Triangulated irregular network) and GeometryCollection. Coordinates for the geometries can be represented in 2D, 3D or 4D (x,y,z,m).
Well-known text#: Well-known text (WKT) is a text markup language for representing vector geometry objects. WKT can represent various geometric objects: Point, MultiPoint, LineString, MultiLineString, Polygon, MultiPolygon, Triangle, PolyhedralSurface, TIN (Triangulated irregular network) and GeometryCollection. Coordinates for the geometries can be represented in 2D, 3D or 4D (x,y,z,m). The binary equivalent for WKT is Well-known binary format.