RE: Time-series spatial data [was: Re: TCGA / Microscopy Imaging Use Case] [SEC=UNCLASSIFIED]

Frans said> It includes concepts from all scales, from quantum particles to the universe itself. Are we ready to take on all these scales?

IMHO we cannot. However, Bruce's use case below, should indeed be in scope (and I think you will find, Bruce, is addressed by our use cases https://www.w3.org/2015/spatial/wiki/Working_Use_Cases, and if it is not, it should be!).

I expect we can also cover Erich's "morphology of disease at the cellular and sub-cellular levels" as that should fall out of what we are doing anyway.
Erich, can I encourage you to join up to be sure of that?

Kerry




From: Bruce Bannerman [mailto:B.Bannerman@bom.gov.au]
Sent: Tuesday, 10 March 2015 12:21 PM
To: Frans Knibbe | Geodan; public-sdw-wg@w3.org
Subject: Time-series spatial data [was: Re: TCGA / Microscopy Imaging Use Case] [SEC=UNCLASSIFIED]

Hi Frans,

(I've been lurking on this list for a little while now...)

I noted the following in your last comment.

"I think that on the human/macroscopic/geographical level space and time can be kept separate, in the sense that a model or ontology for space does not really need time concepts, and vice versa."

>From my perspective in managing climate data, all of our data is time-series spatial data. It is very important to understand the 'when' as well as there 'where' with respect to the data (together with the data provenance etc).

For example, we manage and analyse point-series and gridded distributions of say average maximum temperature over a wide range of time periods from daily, monthly, yearly, decadal etc periods.

When using this data, it is *critical* that we understand the temporal period that the spatial data refers to.

For more information on what we mean by climate data, can I refer you to WMO No. 1131, Climate Data Management System Specifications [1], Section 4, Time Series Climate Data.

Bruce

[1] http://library.wmo.int/opac/index.php?lvl=notice_display&id=16300#.VP5F9DUu6Fg




From: Frans Knibbe | Geodan <frans.knibbe@geodan.nl<mailto:frans.knibbe@geodan.nl>>
Date: Tuesday, 10 March 2015 11:42
To: "public-sdw-wg@w3.org<mailto:public-sdw-wg@w3.org>" <public-sdw-wg@w3.org<mailto:public-sdw-wg@w3.org>>
Subject: Re: TCGA / Microscopy Imaging Use Case
Resent-From: <public-sdw-wg@w3.org<mailto:public-sdw-wg@w3.org>>
Resent-Date: Tuesday, 10 March 2015 11:42


Wow, that is an interesting use case. Maybe this may calls for a better definition of what we mean by 'spatial data'?

I remember the time when geographers started switching from using the adjective 'geographic' to using 'spatial', implying a broadening of scope and a higher relevance. But still the actual topics were macroscopic objects, things that you can plot on a map. And the reference systems still are earth based.

Taken literally, 'spatial' covers a lot more than 'geographic'. It includes concepts from all scales, from quantum particles to the universe itself. Are we ready to take on all these scales?

Related to the issue of the scope of scale is the relationship between space and time. I think that on the human/macroscopic/geographical level space and time can be kept separate, in the sense that a model or ontology for space does not really need time concepts, and vice versa. But it could well be that such a separation is not possible for very small things (like elemental particles) and very big things (like galaxies). On such levels time and space tend to be more entangled.

Greetings,
Frans

On 2015-03-03 21:52, Erich Bremer wrote:

 Studying the morphology of disease at the cellular and sub-cellular levels using high resolution tissue images is extremely important to help understand the nature of various cancers. The Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov/) contains over 32,000 de-identified whole-slide microscopy images (WSI) of over two dozen cancer types. These images can contain between 100K-1M nuclei each.  Biomedical informatics researcher have developed (and continue to develop) software to automatically segment nuclei for study.  The spatial features of each nucleus and groups of nuclei as it relates to other nuclei combined with other linked data such as other morphological features (crypts, ducts, etc) and/or patient lab results are used in analyzing and categorizing tissues and patients into groups and in comparing such groupings to understand disease mechanisms in a particular cancer type as well as across cancer types.

Representing nuclear segmentations is often done with binary masks or through polygon representations (e.g., the use of Well Known Text (WKT) representations) and also by leveraging work from the Geospatial community.  However, in the case of nuclear segmentations, coordinate systems are 2D & 3D Cartesian based.  Although the majority of work is this area is 2D-based, a growing segment of microscopy is also 3D-based as the technology develops and become more sophisticated.  As living tissue can change over time through growth, infection, cancer, damage, etc, (as well as its associated organism's various properties) it is important that spatial locations of features such as nuclear segmentation be also represented in a temporal aspect for proper comparisons.

Samples of TCGA WSI data can be viewed at: http://cancer.digitalslidearchive.net


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