Day: August 8, 2011

“When will ArcGIS Run on a Mac?”

/ Matt Artz / 10 Comments

Esri logo

Update, 11 July 2014:

Will Esri be migrating ArcGIS to the Mac?

We currently have no plans to migrate the full ArcGIS for Desktop to the Mac OS. It does, however, run very well on Mac using Windows emulation software (e.g., Bootcamp). This is a high-performance environment, and many of our developers at Esri use the Mac environment as their platform.

In addition, Explorer for ArcGIS will run natively on the Mac OS. Similar to the iOS app, it will work with web maps and access ArcGIS for Server services (both on premises and hosted). Explorer for ArcGIS on the Mac will be available at the 2014 User Conference.

Developers can develop native apps for Mac OS using the ArcGIS Runtime SDK for OS X.

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Note: in the ~2 years months since the post below was written, some things have changed.  The 2013 Esri User Conference Q & A (July 2013) notes: “Q: Can Esri develop a simple Mac-based viewer for ArcGIS Online?” – See the answer at: http://events.esri.com/uc/QandA/index.cfm?ConferenceID=CCAEEE69-1422-2418-7F1D0EB8490B776D#sthash.V7J5INEI.dpuf . Also, “Q: When will Esri support native Mac OS and Mac hardware?” – See the answer at: http://events.esri.com/uc/QandA/index.cfm?ConferenceID=CCAEEE69-1422-2418-7F1D0EB8490B776D#sthash.V7J5INEI.dpuf.

In the late 1980s, Macintosh computers were commonplace on the desks of Esri staff.  People used them to write documentation, design graphics, and write proposals.  About the only thing we didn’t do with them was run Esri software.

Esri actually did make software that ran on the Apple platform at one time.  This was back in the early 1981, when a short-lived product called “Gridapple” was released for the pre-Mac Apple II platform.  Gridapple was the first microcomputer implementation of Esri’s raster-based “Grid” system.

In the early 1990s, during the early days of ArcView, Esri was working on a port of ArcView to the Macintosh, but due to engineering challenges it never got beyond pre-release.

As it does every year, the question came up again at the 2011 Esri International User Conference: “When will ArcGIS Run on a Mac?” This question was addressed at the UC Closing Session on Friday.

“We’d love to be on the Mac, but we have engineering priorities…so we have to ask ourselves what’s most important for our users,” said Jack Dangermond.  “That focus is very important and we want to make sure that we don’t spread our resources too thin. In theory, we could spread our resources more on platforms and thus less on functionality. But would you want us to slow down advancement of the basic tool in order to deploy on a Mac?”

Of the approximately 1,600 people attended the Closing Session, only one or two people raised their hands.

But don’t count a Mac version of ArcGIS out yet.  Dangermond added “We’ll probably start moving more towards supporting the Mac at the next release after ArcGIS 10.1.”

Scott Morehouse provided a little more detail: “The architecture changes we’re making with ArcGIS 10.1 are allowing us to break some of the deep integration with Windows that we’ve built through the component architecture. That complexity of moving to the Mac is getting easier because the component architecture is going away. This is helping us work better on Linux and other environments and that engineering work does allow us to work more on building a native Mac OS application. The problem is figuring out how to allocate resources to support Window versus Mac or the lightweight clients like Android, mobile hosted services, etc. So it’s a technical problem. It’s also a resource problem.”

Meanwhile, Apple users are not left high and dry—there are several options available to let you use your Apple devices as entry points into the ArcGIS system. “I see this issue three ways,” said Chris Cappelli, a self-professed Mac bigot. “First, we’re aggressively supporting the iOS platform for mobile Mac users so they can leverage online GIS and the ArcGIS system. Secondly, our browser-based products like ArcGIS Explorer Online can be used on a Mac. When I need to make maps on my Mac, I always use ArcGIS Explorer Online. Lastly, if I ever need to run ArcGIS Desktop, I have a virtual machine that runs Windows 7. I can run ArcGIS Desktop from there.”

Foot and Mouth Disease Revisited: Re-analysis using Bayesian Spatial Susceptible–Infectious–Removed Models

/ Matt Artz

Spatial and Spatio-temporal Epidemiology, Available online 19 July 2011

Andrew B. Lawson, Georgiana Onicescu, and Caitlyn Ellerbe

“The Foot and Mouth disease (FMD) outbreak in the United Kingdom in 2001 was modeled via the use of Bayesian spatial susceptible-infected-removed (SIR) models. In these models the underlying mean of the incident cases was modeled spatially and in time. Dependence structures at the parish level between previous and current cases were modeled either with individual dependence or with neighborhood dependencies. Additional confounding was modeled via random effects that can have either uncorrelated or spatially correlated prior distributions. The best models found relied on lagged population and infection count within the same parish but neighborhood lagged dependencies overall did not provide a good fit. Models with only a space-time interaction effect were preferred over more complex models. The estimation of ‘decline’ markers for different areas was considered via difference operators as posterior functionals. These proved to be useful in giving an early indication of the waning phase of the epidemic locally.”

Brain’s Map Of Space Falls Flat When It Comes To Altitude

/ Matt Artz
Firing pattern from a grid cell recorded on a flat surface.

Firing pattern from a grid cell recorded on a flat surface. Credit: Kate Jeffery/UCL

Animal’s brains are only roughly aware of how high-up they are in space, meaning that in terms of altitude the brain’s ‘map’ of space is surprisingly flat, according to new research.

In a study published online in Nature Neuroscience, scientists studied cells in or near a part of the brain called the hippocampus, which forms the brain’s map of space, to see whether they were activated when rats climbed upwards.

The study, supported by the Wellcome Trust, looked at two types of cells known to be involved in the brain’s representation of space: grid cells, which measure distance, and place cells, which indicate location. Scientists found that only place cells were sensitive to the animal moving upwards in altitude, and even then only weakly so.

Professor Kate Jeffery, lead author from UCL Psychology and Language Sciences, said: “The implication is that our internal sense of space is actually rather flat – we are very sensitive to where we are in horizontal space but only vaguely aware of how high we are.

“This finding is surprising and it has implications for situations in which people have to move freely in all three dimensions – divers, pilots and astronauts for example. It also raises the question – if our map of space is flat, then how do we navigate through complex environments so effectively?”

How the hippocampus makes its map of space is fairly well understood for flat environments, but the world is of course not flat – it has a richly varied topography, and a useful map therefore needs to work in all three dimensions. However, adding a third dimension to the two horizontal ones makes things very much more complicated for a map, and it is not clear how – or even if – the brain can encode this.

To begin to answer this question scientists looked at neurons known as grid cells, which become active periodically and at very regular distances as animals walk around, forming a grid-like structure of activity hot-spots. Previous work has found that grid cells are largely concerned with marking out distances.

In the study, rats walked not just on flat ground but also on pegs on a climbing wall, or else on a spiral staircase, so that the rats moved not only horizontally but also vertically. Interestingly, the grid cells still kept track of horizontal distance but did not measure out vertical distances. It seems as if grid cells do not “know” how high they are.

In the second part of the study scientists looked at another type of neurons known as place cells. Place cells, found in the hippocampus itself, produce single activity hotspots in the environment and seem to function to encode specific places. These neurons were only weakly sensitive to height too – but they did show some responsiveness, suggesting they received information about height from some other, possibly non-specific, source.

Professor Jeffery said: “It looks like the brain’s knowledge of height in space is not as detailed as its information about horizontal distance, which is very specific. It’s perhaps akin to knowing that you are “very high” versus “a little bit high” rather than knowing exact height.”

[Source: UCL press release]

3D Modeling of Light Interception in Heterogeneous Forest Canopies using Ground-based LiDAR Data

/ Matt Artz

International Journal of Applied Earth Observation and GeoinformationInternational Journal of Applied Earth Observation and Geoinformation, Volume 13, Issue 5, October 2011, Pages 792-800

Dimitry Van der Zandea, Jan Stuckens, Willem W. Verstraeten, Simone Mereu, Bart Muys, and Pol Coppin

“Highlights:

  • Voxel-based Light Interception Model to estimate forest canopy/radiation interactions.
  • Estimation of the percentage of above canopy light at any point in 3D canopy.
  • Comparison with reference data yielded a mean absolute error of 5.78%.

“A methodology is presented that describes the direct interaction of a forest canopy with incoming radiation using terrestrial LiDAR based vegetation structure in a radiative transfer model. The proposed ‘Voxel-based Light Interception Model’ (VLIM) is designed to estimate the Percentage of Above Canopy Light (PACL) at any given point of the forest scene. First a voxel-based representation of trees is derived from terrestrial LiDAR data as structural input to model and analyze the light interception of canopies at near leaf level scale. Nine virtual forest stands of three species (beech, poplar, plantain) were generated by means of stochastic L-systems as tree descriptors. Using ray tracer technology hemispherical LiDAR measurements were simulated inside these virtual forests. The leaf area density (LAD) estimates derived from the LiDAR datasets resulted in a mean absolute error of 32.57% without correction and 16.31% when leaf/beam interactions were taken into account. Next, comparison of PACL estimates, computed with VLIM with fully rendered light distributions throughout the canopy based on the L-systems, yielded a mean absolute error of 5.78%. This work shows the potential of the VLIM to model both instantaneous light interception by a canopy as well as average light distributions for entire seasons.”