GIS

Web Mapping Service (WMS): A WMS is a standard protocol developed by the Open Geospatial Consortium (OGC) in 1999.

Web Feature Service (WFS): A WFS provides essential tools for creating interactive maps with features like search capabilities, filtering, and sorting. Unlike WMS, a WFS gives access to vector data (not raster).

GeoServices REST Specification: The GeoServices REST Specification provides an open way for web clients to communicate with GIS servers by issuing requests to the server through structured URLs. The server responds with map images, text based geographic information, or other resources that satisfy the request.

Geospatial APIs are software abstraction layers that provide standardized methods to query, analyze, and visualize spatial data from diverse sources. They support essential functionalities including 2D/3D map rendering, geocoding, coordinate transforms, and real-time sensor data integration. Modern designs employ RESTful architectures and OGC standards to enhance interoperability, performance, and scalability across geospatial applications.

A geospatial Application Programming Interface (API) is a software abstraction layer—typically a web service or client library—that exposes standardized operations for querying, rendering, analyzing, and modeling spatial data, including vector features, raster coverages, multi-dimensional sensor observations, and geospatial attributes. Geospatial APIs are foundational for scientific computing, urban analytics, planetary research, public health surveillance, and geospatial-AI workflows, enabling programmable access to distributed spatial resources and seamless integration across data repositories, sensor infrastructures, visualization platforms, and analytic pipelines.

Types are meant to be shared across systems, while models are system-specific. Your project structure should reflect this separation.

A well-implemented Taxi ecosystem has clear separation between shared semantics and system-specific implementations.

A mature implementation typically includes: ​ Shared Taxonomy Collection of semantic types Broadly shared across organization Version controlled and carefully governed Published as a reusable package ​ Service Implementations Models and service definitions using types from taxonomy System-specific structures Published to TaxiQL server (like Orbital) Each service depends on shared taxonomy ​ Data Consumers Import shared taxonomy only Don’t depend on service-specific models Query data using TaxiQL Receive data mapped to their needs ​

Best Practices ​ Type Development Focus on business concepts Keep types focused and single-purpose Document type meanings clearly Version types carefully ​ Model Development Use semantic types for fields Keep models service-specific Don’t share models between services ​ Service Integration Publish service contracts to TaxiQL server Use semantic types in operation signatures Let TaxiQL handle data mapping

Measuring Success Your implementation is successful when: Services can evolve independently Data integration requires minimal code New consumers can easily discover and use data Changes to one service don’t cascade to others Semantic meaning is preserved across systems

Because there are usually many functions in a Postgres database, the service only publishes functions defined in the schemas specified in the FunctionIncludes configuration setting. By default the functions in the postgisftw schema are published.

The VRT driver is a format driver for GDAL that allows a virtual GDAL dataset to be composed from other GDAL datasets with repositioning, and algorithms potentially applied as well as various kinds of metadata altered or added. VRT descriptions of datasets can be saved in an XML format normally given the extension .vrt.

gdalbuildvrt [--help] [--long-usage] [--help-general] [--quiet] [[-strict]|[-non_strict]] [-tile_index <field_name>] [-resolution user|average|common|highest|lowest|same] [-tr <xres> <yes>] [-input_file_list <filename>] [[-separate]|[-pixel-function <function>]] [-pixel-function-arg <NAME>=<VALUE>]... [-allow_projection_difference] [-sd <n>] [-tap] [-te <xmin> <ymin> <xmax> <ymax>] [-addalpha] [-b <band>]... [-hidenodata] [-overwrite] [-srcnodata "<value>[ <value>]..."] [-vrtnodata "<value>[ <value>]..."] [-a_srs <srs_def>] [-r nearest|bilinear|cubic|cubicspline|lanczos|average|mode] [-oo <NAME>=<VALUE>]... [-co <NAME>=<VALUE>]... [-ignore_srcmaskband] [-nodata_max_mask_threshold <threshold>] <vrt_dataset_name> [<src_dataset_name>]...

This program builds a VRT (Virtual Dataset) that is a mosaic of a list of input GDAL datasets. The list of input GDAL datasets can be specified at the end of the command line, put in a text file (one filename per line) for very long lists, or it can be a MapServer tileindex (see the gdaltindex utility). If using a tile index, all entries in the tile index will be added to the VRT.

Emphasizing clarity, accuracy, and purpose-driven design, the guide covers how to create compelling static and interactive visualizations tailored to diverse analytical and communicative objectives. Throughout, we will adhere to best practices in cartographic design to ensure that each visualization conveys its intended message clearly and truthfully.

Successful geospatial visualization demands attention to several core principles that ensure the resulting map or graphic is both informative and comprehensible. Visualizations must balance aesthetic considerations with functional accuracy to effectively convey spatial information.

Clarity and Simplicity Clarity in geospatial visualization means the core message of a map is immediately apparent to viewers. Overloading a map with too much information or too many design elements can confuse rather than enlighten. Designers should strive for minimalism—include only the critical spatial features and data relevant to the map’s purpose. An expert data visualization principle is to “remove any elements that do not contribute to understanding the data”, such as excessive labels, grid lines, or decorative clutter. By simplifying the visual display, we direct the audience’s attention to the most important patterns or locations. Key guidelines for simplicity include: Limit the number of thematic layers shown on a single map. Use intuitive symbols and clear labeling for features. Avoid excessive annotation or unnecessary visual noise. In practice, a clean design with ample white space and straightforward symbology will enhance comprehension and engagement. The goal is a map that communicates rather than overwhelms.

Appropriate Color Schemes: Choose color palettes that are perceptually uniform and appropriate for the data. Avoid using arbitrary or excessively bright colors that could mislead or cause eye strain. Instead, use scientifically derived colormaps like viridis or plasma, which are designed to be uniform and colorblind-friendly. For example, a sequential palette (light to dark in one hue) makes sense for a unipolar data range (e.g., population density), whereas a diverging palette (two hues fading to a neutral midpoint) is best for data that have a meaningful center (e.g., above/below average comparisons). Ensure that the colors have sufficient contrast against each other and the map background for readability – legend text and boundary lines should also be clearly visible. (A poorly chosen palette or low-contrast colors can render a map useless to colorblind viewers or even to those in print vs. screen viewing.) Maintain consistency in color use across multiple maps; if one map shows intensity of something in red, another map in the report should ideally use red for that same intensity concept.

Clear Legends and Annotations: Provide explanatory legends, titles, and annotations to guide the viewer. A well-crafted legend is crucial for interpreting a map’s symbology – it should be clear, concise, and not overly cluttered. Viewers should not struggle to match colors or symbols to their meaning. As a cartography guide points out, a confusing legend can undermine your visualization, whereas a clear legend “guides [readers] seamlessly through your data story”. Tips include: place the legend in an unobtrusive but noticeable location (often corners work well), use intuitive labels (e.g., “Population (millions)” rather than “Pop_val”), and avoid too many categories. Additionally, titles and subtitles are important: they frame what the map is about. A good title might state the what/where/when (e.g., “Population Distribution by Region, 2020”) so the audience immediately knows the context. Annotations like callout labels or arrows can highlight key insights (e.g., “Area X has the highest value”). However, avoid excessive text on the map itself; maintain a balance so the map doesn’t become a textbook diagram unless that’s intended. The goal is to inform without overwhelming.