In+ersec+ion for Spatial People

Understanding Simple Features

Introduction

Simple features are the basic building blocks of a simple vector based GIS. This article provides you with a simple definition of simple features and walks you through the conceptual design of a couple simple feature types.
We have two (2) regular features in Digital Surveying Magazine that deal almost exclusively with vector based GIS. The first of these regular features, entitled “JUMP Into GIS”, showcases the application of open source desktop GIS program OpenJUMP to real world GIS projects. The second of these regular features, entitled “Spatial Super Models”, teaches you how to design simple vector GIS data models, or implementation blueprints.
The information in this article will give you the foundation you need to understand the articles in these two (2) regular features of Digital Surveying Magazine.
We will begin our article by providing you with a basic definition of simple features.

A Basic Definition of Simple Features

What is a simple feature in the context of a discussion about vector GIS?
I have personally found this definition is helpful:
A simple feature is the digital representation of a real-world geographic feature in a GIS.
We can expand our basic definition of a simple feature with this additional information:
A simple feature has two primary elements. The first is a geometry used to represent the shape and location of the geographic feature. The second is a set of basic data attributes that describe the geographic feature’s non-spatial characteristics.
We can further increase our understanding of simple features by considering the Simple Feature Type.

Simple Feature Types

A simple feature type defines a set of requirements that all simple features belonging to that type must obey. They are essentially an organizational tool to help classify simple features.
The most important set of requirements defined by a simple feature type are the data types of the simple feature’s geometry and set of attributes.
These concepts will become more clear with a couple of concrete examples.
Let’s conclude our article with those two (2) examples.

Example #1: Modeling Land Parcels as Simple Features

In our first example we’ll imagine we are building a GIS to manage survey data about land parcels.
What geometry type should our land parcel simple feature type define for land parcel simple features?
A polygon seems like the geometry data type choice that makes the most sense for this simple feature type.
What non-spatial attributes would a land surveyor like to know about a land parcels? What data types would we use to model those attributes?
Most surveyors would be interested in the following attributes:

1) Identity of the most recent vesting grant deed.
2) Tax assessor parcel number.
3) Identity of the written instrument that created the parcel (survey map or deed).

What data types would we use to store these attributes for each simple feature? We would likely use a simple text value or numeric value for each of these attributes, depending on how the values are represented.
Diagram #1 shows a graphical representation of our land parcel simple feature.

Example #2: Road Segment

In our second example we’ll imagine we are building a GIS for a local City road maintenance department.
What geometry type should our land parcel simple feature type define for road segment simple features?
A linear geometry like a simple line segment seems like the geometry data type choice that makes the most sense for this simple feature type. (Depending on the map scale, you might also use polygons to represent the actual footprint of a road segment.)
What non-spatial attributes would a maintenance supervisor like to know about a road segments? What data types would we use to model those attributes?
A road maintenance supervisor would be interested in the following attributes:

1) The width of the physical road surface.
2) The paving material type.
3) The date of the last maintenance activity on the segment.
4) The type of maintenance activity last performed.

What data types would we use to store these attributes for each simple feature? We would likely use a simple numeric value for the width attribute, simple text value for the paving material type, a date data type for the third attribute, and a simple text value to identify the type of the maintenance activity last performed.
Diagram #2 shows a graphical representation of our land parcel simple feature.

Conclusion

This article provided you with a basic definition of simple features, explained the requirements put into place by a simple feature type, and looked at two (2) examples of how simple features could be designed.
Future articles in the Digital Surveying Magazine newsletter will look in more depth and simple features, their design, and their role in a GIS.

This article was shared with Slashgeo in a media partnership with Digital Surveying Magazine.

UC Berkeley GIS Day 2012

A GIS Day 2012 Event was held at US Berkeley's Mulford Hall . The event was co-hosted by the Bay Area Automated Mapping Association and the Geospatial Innovation Facility, with support from the Northern California Region of the Association for Photogrammetry and Remote Sensing. Landon Blake, the Assistant Editor of Digital Surveying Magazine, was at the event, and provided media coverage of the event for Digital Surveying Magazine and Slashgeo. In this article of the newsletter Landon shares his summary of the event.

Cynthia Powell: Climate Space Trends In Yellowstone National Park

At 5:30 in the evening Cynthia Powell of Ecoclim gave the talk entitled “Climate Space Trends in Yellowstone National Park”. The talk provided a summary of the work performed by Ecoclim analyzing the minimum and maximum temperatures and amount of rainfall in Yellowstone National Park over the past 100 years.

As part of this project, Ecoclim attempted to answer three (3) questions.

1. How do we measure if the climate is changing within the park?

2. If the climate is changing, what are the spatial and temporal patterns of that change?

3. How can land managers in and around the park use this information?

One aspect of the analysis discussed by Cynthia dealt with Canadian Lynx habitat. Yellowstone National Park is home to the southernmost finger of the Lynx habitat. This “habitat finger” is especially susceptible to climate change because of its southerly location. The work Ecoclim performed in the park examined how the Lynx habitat would changed in the future based on different climate models.

An interesting concept discussed by Cynthia in the talk was that of “climate refuges”. These are areas within the park that appear to be resistent to changes in temperature and rainfall brought about by climate change. This resistance could be due to microclimate factors. It will be important to preserve these climate refuges for climate sensitive species like the Canadian Lynx. Information about the location of these climate refuges can help land managers make land use and land conservation decisions at the park.

Brian Quinn: Community 1:1000 Base Mapping

At 6:00 in the evening Brian Quinn described the development of a GIS basemap for Marin County. Marin County now has a set of basemap tiles for 10 scales or “zoom levels” between 1:1080 and 1:570,000.

Brian described two (2) challenges encountered during the basemap creation process. The first was discrepancies that became apparent when GIS data layers are overlaid on one another. The second is that GIS data layers maintained by the various county departments are not always suitably designed for cartographic representation.

Brian also described two (2) benefits from having a standard basemap at different scales available to County staff. The first benefit is the elimination – of time employees previously wasted in developing a custom basemap for every mapping project. The second benefit is the delivery of county map products with a consistent basemap background.

One unexpected benefit of showing structures on the basemap is improved response time for emergency responders using the County GIS data. For example: Many residents of Marin County live on the water in houseboats or other floating structures. These floating residences are not usually accessible from a public street like a typical residence. The Marin County basemap allows emergency responders to determine how to best reach a particular floating residence from the nearest public street

Brian also mentioned the County is no longer using photogrammetry for to map terrain in the County basemap. Photogrammetry has been replaced with LIDAR data for all of the County's basemap terrain needs.

Jeremy Wood: Location Anonymization

At 7:45 Jeremey Wood of locationanonymization.com gave a talk on his process for making geospatial data more “anonymous”. According to Jeremey stripping GPS tracks provided by a consumer doesn't make it sufficiently anonymous. The consumer can be still be identified by his travel patterns and the locations he regularly travels to (for example: their home or office). Jeremey's anonymization process involves intentionally degrading location data when a consumer travels outside of a “public area”.

Although Jeremey's concept was intriguing, the definition of “public areas” versus “private areas” in which location data would be intentionally degraded seems to be a critical element that could prove challenging.

This article was shared with Slashgeo in a media partnership with Digital Surveying Magazine.

https://www.coursera.org/course/maps

Learn how advances in geospatial technology and analytical methods have changed how we do everything, and discover how to make maps and analyze geographic patterns using the latest tools.

The world's first geo-MOOC. At least according to Directions Magazine.

[MOOC stands for massive open online course]

I'm abroad this week, expect return to normal geonews coverage next week, thank you for your patience.

Slashdot discusses a story named Why Hasn't 3D Taken Off For the Web?

Their summary: "With HTML5 we're closer to the point where a browser can do almost everything that a native app can do. The final frontier is 3D, but WebGL isn't even part of the HTML5 standard, Microsoft refuses to support it, Apple want to push their native apps and it's not supported in the Android mobile browser. Flash used to be an option but Adobe have dropped mobile support. To reach most people you'd have to learn Javascript, WebGL and Three.js/Scene.js for Chrome/Firefox, then you'd have to learn actionscript + flash for the microsofties, then learn objective c for the apple fanboyz, then learn Java to write a native app for Android. When will 3D finally become available for all? Do you think it's inevitable or will it never see the light of day?"

We mentioned WebGL often lately, with many geospatial web tools betting on it.

Google is not alone trying to take advantage of trillions, Slashdot runs a story named Oxford Tests Self-Driving Cars.

Their summary: "Using advances in 3D laser mapping technology, Oxford University has developed a car that is able to drive itself along familiar routes. This new self-driving automobile uses lasers and small cameras to memorize everyday trips such as the morning commute. This car is not dependant on GPS because this car is able to tell where it is by recognizing its surroundings. The intent is for this car to be capable of taking over the drive when on routes that it has traveled before. While being driven, the car is capable of developing a 3D model of its environment and learning routes. When driving a particular journey a second time, an iPad on the dashboard informs the driver that it is capable of taking over and finishing the drive. The driver can then touch the screen and the car shifts to 'auto drive' mode. The driver can reclaim control of the car at any time by simply tapping the brakes."

I'm currently abroad but wanted to share the good news that Landsat 8 Satellite Successfully Launches Into Orbit.

The Slashdot summary: ""The Landsat Data Continuity Mission is now in orbit, after launching Monday from Vandenberg Air Force Base in Calif. After about three months of testing, the U.S. Geological Survey will take control and the mission, renamed Landsat 8, will extend more than 40 years of global land observations critical to energy and water management, forest monitoring, human and environmental health, urban planning, disaster recovery, and agriculture." We still need more new observation satellites to avoid losing Earth observing capabilities as the work horses of the NASA/USGS fleet die of old age."

Bloggage update: Hard to believe it's over a year since I left Kuwait - so just out of curiosity I looked for web maps of the area again. Google and OpenStreetMap left a little to be desired in the geography, but Leaddog's Syria GIS Map is very impressive indeed. Then I turn to my old employ's arcgis.com map of a Kuwait University project. It's a full Kuwait Municipality map, with GIS and geodesign of the new university, but also the whole city complete with directions including barriers, which I haven't seen on Google or Bing maps!

While cleaning up old emails I ended up on this scientific article named Metrics to Measure Open Geospatial Data Quality published last year by Jingfeng Xia. Data quality is a topic we discussed before.

From the conclusion: "Because of the uniqueness and complexity of geospatial data, quality control is always a challenge to data providers, managers, analysts and data service providers. Metrics developed to measure data quality need to reflect the nature of the data, and therefore must be diversely structured to handle maps, coordinates, attributes and other types of geospatial data. A list of dimensions with clear and accurate definitions will provide necessary standards for the measurement. When the practice of open access is also considered, several more layers of complexity are added and additional tasks are created to solve issues pertaining to web communication, data usability, data integrity and related issues. Both quantitative metrics based on objective measurement and qualitative metrics based on subjective measurement are essential to the quality control of geospatial data."

Major news for geospatial open source, earlier this week the LocationTech Initiative was launched by the Eclipse Foundation.

From the official press release: "The Eclipse Foundation has launched a new initiative to support user driven development of location aware systems. The new LocationTech working group will allow companies to jointly develop and deploy components that bring location awareness to Enterprise IT. The Eclipse LocationTech initiative is led by Oracle, IBM, OpenGeo, and Actuate."

Its 4 initial areas of focus:

• Storage and processing of massive data volume
• Model driven design
• Desktop, Web, and mobile mapping
• Real time analysis of business critical data

I liked Direction Mag summary of how LocationTech complements the OGC and OSGeo: "In particularly, LocationTech offers a “full-service not-for-profit Foundation providing support for open source location aware technologies.” He ticked off this list of differentiators: 

• software licensed under commercial friendly open source licenses
• intellectual property (IP) management services
• a professionally-supported open source forge
• full-service, not-for-profit staffed by full-time professional staff to support the ecosystem"

Some time already since we heard of this open source geospatial ETL tool, GeoKettle 2.5 has been released. Unless I'm mistaken, the other open source geospatial ETL tool formerly named 'Spatial Data Integrator', now known as 'Talend Spatial Module', is at version 5.2.1.

From the announcement: "GeoKettle 2.5’s new features include:

• WFS (OGC Web Feature Service): Input step for the retrieval of geospatial data directly from an OGC Web Feature Service;
• CSW (OGC Catalogue Service for the Web): Input and Output steps, introduced in version 2.0, that now support reading from Deegree and MDWeb CSW. The CSW Output step now supports transactions (metadata insertion, update and deletion), tested with in Deegree and GeoNetwork;
• WPS (OGC Web Processing Service): Client step has been added to invoke remote geoprocesses exposed as WPS, briging new geoprocessing capabilities, tested with Deegree, GeoServer, PyWPS, Zoo WPS and 52North WPS implementations;
• Use of the new version (1.9.1) of the GDAL/OGR codebase for the OGR Input and Output steps for even more advanced and powerful capabilities;
• Use of the new version (1.13) of the JTS library to benefit from performance enhancements and bug fixes;
• New conversion functions have been added to the Calculator step (more build and convert capabilities with WKT, GeoJSON, GML, KML, …);
• Use of an up-to-date version of the EPSG database, the definition of custom projection introduced in version 2.0 remains possible;
• Dedidicated installers (Windows, Linux / Debian or Linux / Red Hat, Macintosh) are now available, making the installation experience easier (zip archive still available);
• Sextante Module: Adds advanced vector-based geoprocessing capabilities based on the Sextante library."