Geospatial Models and Representations

In one feature data set, point, line and polygon geometries can be combined. Explore the different types of feature layers by clicking and inspecting popups.

• Points -  Natural Monuments 
• Lines -  Bike Paths 
• Polygons -  Nature Reserves 

We will further explore this data set in the 'Introduction to Spatial Data' web course.

From the ArcGIS Online Content tab, you will create a layer based your own ideas, add attributes and domains and thus establish a full schema. Based on this you will add and edit sample data.

It will be important to fully document your feature layer, so that it can be well understood and found at a later time again.

The geometry and associated attributes of feature classes will not necessarily originate from the same sources or at the same time. Think about a world countries data set, and demographic or economic data collected by different institutions.

Using an agreed unique identifier ('database key') like 'AT' or 'AUT' for Austria, tabular data can be associated ('joined') with the geometric polygon features representing countries.

Being familiar with digital photographs, and pixels as their building blocks, we easily understand aerial and satellite imagery digitally representing areas on Earth's surface.

Image pixels are defined by colours (e.g. with red-green-blue components). Processing these colours in combination with topographic and temporal context, and based on 'ground truth' sampling, land cover rasters are generated.

This transition from radiometric reflectance ('colour') to thematic data is an important workflow in Geoinformatics, and a major application domain of the raster data model.

In most cases we will be working with square raster cells - actually requiring a projected spatial reference system. Exceptions are global grid data sets defined in fractions of degrees longitude / latitude.

Spatial resolution - the size of individual grid cells - is the main characteristic of any raster. This, of course is not to be confused with positional accuracy.

Each raster cell is considered a homogeneous, elementary building block in a raster layer. Cell values represent reflectance 'colours' in the case of images or refer to a legend entry for thematic rasters.

Land cover is one example of a spatially discrete phenomenon: land cover classes have crisp boundaries, inside one class all cells are considered identical.

Temperature and other weather or climate observations, though, will vary gradually over any area. Adjacent cells typically will have slightly different values within an overall range. We refer to these as spatially continuous layers, and as thematic or topographic 'surfaces'. Terrain data in the form of raster Digital Elevation Models (DEM) are typical examples of spatial continuity.

Discrete phenomena typically are represented by nominal-level categorical data, while surfaces require a metric level of measurement.

Vector layers are sometimes converted into raster representations, e.g. to facilitate multi-criteria overlay analysis within the raster domain.

This is a non-reversible process, typically data layers will be maintained in their original formats and only temporarily converted as needed.

Measurements at weather stations (or other local samples of spatially continuous phenomena) are converted into raster surfaces by spatial interpolation - a large family of spatial analysis methods.

Feature data layers provided as services offer a flexible and dynamic online working environment.

Individual OSM layers can be added, and queried, by accessing them through service interfaces. A few examples (you might need to zoom in further):

•  streets , of course
•  waterways 
•  buildings 
•  tourist locations 
•  points of interest 

As all these services are accessible through open protocols, they can be integrated and used across all internet platforms and tools.

On the ArcGIS Online platform, we do have the opportunity to create feature services from feature layers by going through the 'Publish' workflow.

This allows control of access, editing rights and search facilities.

[insert example from previous example of establishing a feature layer, ev video; share link for viewing]

Feature services sometimes require selective sharing and publishing without changing the overall service definition. This is the case when differentiating access rights, or providing a subset of features and / or attributes.

This is achieved by publishing 'views' referring to original services, but a filtered and modified perspective for users and applications.

...

Imagery and thematic raster data require a different approach to sharing. Optimisation for fast display can be traded against access to original pixel values and readiness for analysis and classification.

For now, we just take note that feature and image services are different, but both provide full online access to geospatial data.

Not all geospatial data represent stable 'steady states' of geographic phenomena. With the increasing availability of connected sensors, we have the opportunity of a near real time monitoring of our planet, and also reaching into local scales all the way to 'smart buildings'.

Explore the 'live' access to local weather by clicking on some of the arrows on this map, or explore live runoff data through the button below:

Monitoring spatial dynamics in real time, through remote sensing, mobile sensors or in-situ observations helps creating a view of the world-at-our-fingertips, further establishing a truly Digital Earth.

While 'FAIR' does not necessarily mean 'open', this principle offers a quick and easy summary of important criteria for sharing data and services:

1. Data need to be findable through metadata, catalog entries and registered portals
2. Data need to be retrievable through unique identifiers, w/o authentification, and open protocols
3. Data allow integration with other data, workflows, applications by following open standards
4. Data follow a clear usage license, with source context and semantics documented to allow reuse

Open data products and services are available under a open license (not 'no license', as frequently misunderstood). Looking at the Austrian  basemap.at  as an example, which is  published under a CC-BY  license.

 Creative Commons  now is widely established as a flexible open licensing framework, stating the conditions for (re-)use of the licensed materials.

Remembering the FAIR principle, we need a 'discovery infrastructure' to find (open) data. These frequently are national level or regional portals, like the official portal for European data:

It is important to become familiar with (open) data portals in your region and domain, and to practice search including criteria answering the questions of where, when, what, who, why etc

This association of National Mapping agencies publishes  https://www.mapsforeurope.org , where either download links or WFS/WMS/WMTS access tokens can be requested.

This is just one of many examples of trans-national initiatives requiring not only adherence to open service standards, but also homogeneisation of underlying data.

While traditionally spatial data creation was firmly in the hands of state institutions, modern technologies have enabled to citizens to contribute their local and domain-specific knowledge. OpenStreetMap may be the best known platform for crowd-sourcing, but many other communities contribute either to local initiatives through participatory GIS or even citizen science projects.

The INSPIRE - Infrastructure for Spatial Information in Europe - initiative is based on an EC directive with legal status in EU member countries. It mandates the establishment of a community geoportal as an access point to the Member States infrastructures through network services. 

Explore the ' Thematic Viewer ' and identify a sample data set in your field of interest.

You are welcome to practice the design and implementation of mobile / field data acquisition in the context of your professional domain and activities.

If there is no immediate task set for you, think about an interesting and relevant participation initiative in your personal environment. Spatial technologies empower citizens, local community initiatives and grassroots efforts - you certainly will find a worthwhile cause somewhere near you!

Increasingly, local governments use GIS-enabled frameworks to engage with citizens and to secure their participation in planning and decision making. Consider this as a potential experimentation space for your app development.