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# Geoinformation - Science topic

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- The graphic representation to show the importance of using geoinformation in public policies can facilitate the perception, perception and understanding of the user;
- A schema can more easily show at which stage of public policy the geoinformation is used.
Sven Schade Many thanks for the reply. I am very grateful for always responding, as are colleagues who have also responded. Your statements are fundamental to elaborate this representation. The suggestion to categorize the policies and forms of geoinformation that are used is a strategy I can follow. Your comments were very important. Thank you very much.
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I've come across a couple of publicly available hyperspectral image datasets from the website http://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes
Now, my concern is regarding whether or not we can use these datasets directly for building machine learning based models.
Are these datasets geometrically and atmospherically correct? Or do these datasets further require atmospheric and geometric corrections prior to building any machine learning based model?
Also, how can one be assured that a captured hyperspectral image scene is atmospherically and geometrically correct?
As Mr Nasir Hameed suggested, if you are looking forward to inspecting individual spectral signatures in a hyperspectral image, the Digital Numbers (DN) are more than adequate to design an autonomous classification, while atmospheric and geographic corrections would be unnecessary for the framework.
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Spatial Data Infrastructure (SDI) or Geographical Data Infrastructure (GII) is known for sometimes for providing the need for spatial science research as well as many other purposes in science and practice. I am looking forward to the further explanation on the question around - how does the contemporary topic of "Research Data Infrastructure" on spatial science will make a difference in regard to "SDI or GII"?
Spatial Data Infrastructures provide a platform that offers an interaction between spatial data, with networks, standards and policies. This framework can be used to develop institutional arrangements, legal and political tools, social resources to facilitate the integration of spatial data and its maximum potential use. To do this, there must be a channel involving data providers, resellers and users, helping to interact with technical, policy and standards tools to coordinate and use the data correctly. SDI offers minimization of costs and time when integrating data from multiple sources. However, this has not yet been achieved. More research is needed to achieve efficient data integration in the context of the SDI project; the results can assist in the development of technical, political, institutional and management tools. When this is achieved, data integration will facilitate sustainability.
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In Blogs “Maps” as Volunteered Geographic Information http://sdi4apps.eu/2016/09/maps-as-volunteered-geographic-information/ and Enhanced JSON schema for interchangeable Map Compositions http://sdi4apps.eu/2016/09/enhanced-json-schema-for-interchangeable-map-compositions/ Runar Bergheim introduced concept of Map composition as solution for sharing Maps (as composition of different services). Reason is, that basic OGC services like WMS or WFS are not enough for exchanging of more complex maps and also are too technical. So we are now working on this our concept. I would like discussed, if you know something beyond Web Map Context? And if you think, that we need something such?
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I would like to meet any researcher/expert in this area from a German University of Research Institution?
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Dear all,
I have some rainfall data from raingauges over a mountainous area in an arid region of Ethiopia. In need to have raster rainfall map from the point data, but due to high variability of altitude between the raingauges sites, I don't believe Thyessen polygons are the best solution.
I was thinking about IDW, but is there any method which considers DEM?
I am working on QGIS
Hi,
I suggest to use a regression function. Use x, y coordinates AND height as depending variables and the rainfall as independent variable. Based on the regression function, you may calculate for each raster cell the interpolated rainfall value.
I've got some good results with this method, as it will really consider the height dependancy of the rainfall.
Have a nice day
Dietrich
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Recently, the very useful program 'Geoplot' can not be used in the PC in China. This brings a lot of inconvenience to the geology researchers when they need to analyze the geochemical data. Are there some similar programs or Excel add-ins that stay in service in the other areas? And, I will appreciate that if someone could give their names or download addresses.
Dear Yu Sheng Zhu,
if you are on Windows, I would propose that you use our R package called GCDkit.
As one of the authors I cannot judge whether it is "convenient". But what I can say is that it is a freeware, available to download from www.gcdkit.org. It also leaves the possibility of using plain R, with the wealth of its statistical functions, open.
We have recently published a book dealing with using GCDkit, with or without R, in plotting/interpretation of geochemical data and protogenetic modelling that should give you a head start::
Janoušek, V., Moyen, J. F., Martin, H., Erban, V. & Farrow, C. M. (2016). Geochemical Modelling of Igneous Processes – Principles and Recipes in R Language. Bringing the Power of R to a Geochemical Community. Springer-Verlag, Berlin, Heidelberg, 346 pp. doi:10.1007/978-3-662-46792-3
I trust that this helps,
Vojtech Janousek
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Hi everyone,
I am trying to subset a netcdf file with 24 variables (excluding dimension variables: units: days since 1-01-01 00:00:00; calendar: 365_day) and 4 dimensions. The file is ca. 55GB (a global data set).
I would like to create a new .nc file that only included variable information for the summer months (JJA) and the 'daytime' (9am-4pm). The time dimension is defined by the international data-line (midnight). So when the data for the 'daytime' is extracted, it needs to be adjusted to the time differences.
I am most familiar with R and ArcGIS but I have struggled to find examples that would explicitly address this.
Any help would be greatly appreciated!
Thanks, Sven.
Hi Pratiman Patel
NCOs are really good thank you
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NCEP data is available from 1961-2005. However I only want to use NCEP from 1975-2005 in SDSM. How can I extract from the given NCEP data for the specific period of 1975-2005?
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MY Digital elevation consists of a series of DEMs merged together in ArcGIS. Now for a reason I have to select a single co-ordinate system for the whole DEM. What projected co-ordinate system should I select as the DEM consists of a series of DEM having different co-ordinate system?
Please overlay UTM grid on your area and see which zone covers most of your area. Select that zone for projected coordinate system of your dem.
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A symplectic action of the $m$-torus $\mathbb{T}^m$ on a compact symplectic manifold $(M,\omega)$ yields a moment map $\mu : M\to\mathbb{R}^m$. I would like to study the convexity of the moment map (the Atiyah-Guillemin-Sternberg theorem) which is based on the connectedness of the fibers $\mu^{-1}(c)$.
1. Why the components of the moment map are Morse-Bott functions?
2. Why the levels of the $\mu$ are connected, when $m=1$?
3. In the case of a completely integrable system (Arnold-Liouville theorem), is it true that the map $f=(f_1,...,f_n)$ (independent functions in involution) is a moment map for the action of the torus?
Thank you!
Look at Thm 3.5.1 page 117 of these notes
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Let M be a surface in R4. We find some relations between any differential elements, but these are contradictory to one of fundamental equations of the surface M. (Gauss, Codazzi and Ricci equations)
Now,  are these relations are founded valid? More precisely, can I take these relations as characterizations of M?
The fundamental equations of submanifolds in a Riemannian manifold are the equations of Gauss, Codazzi and Ricci. These equations relate the curvature tensors, second fundamental form, and normal connection of the submanifolds with the curvature tensor of the ambient space. In particular, when the ambient space is a real space form, the fundamental equations give rise the the existence and uniqueness theorem, i.e. the fundamental theorems of submanifolds.
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Flow of rivers are not only affected by the size and slopes but also of its geometry. Used several software but most do not consider river sinuosity in the calculation.
Dear Ronald,
FLDWAV can model single channel or dendritic systems, straight or meandering channels, or divided channels.

With my best regards
Prof. Bachir ACHOUR
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I'm very interested in use cases with Arc Hydro and your experiences with it. Do you think it has an added value compared to standard Spatial Analyst analyses?
Hi,
ArcHydro is fully dependent on ArcGIS, so no stand-alone use. And it's not comparable to Spatial Analyst in any way, as the tools in the latter are quite basic and don't help for hydrology-related studies (while they are okay for basic land system mapping).
As wrote Daniel, you can use HEC-HMS, but many models exist, more or less using ArcHydro or Spatial Analyst to generate inputs or processes. A very popular yet complicated model is SWAT (Soil Water Assessment Tool) that has an ArcGIS extension, but also WEPP, and the list is quite long, for extensions and for stand alone applications as well.
It finally depend if you're interested in process-based (hydrological model) modeling or empirical modeling (ArcHydro).
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Can anyone help me on knowing the applications where GIS optimization techniques are used? And any idea if I want to find optimum locations for placement of PMUs in electrical network - What are all the data I have to consider??
If you are new to the topic of Spatial Optimization then I recommend that you have a look at 2009 R.L. Church and A.T. Murray. Business Site Selection, Location Analysis and GIS (New York: Wiley). Church and Murray are two of the leading experts in the area of spatial optimization and their book is very approachable for starters. You can also look up their papers if you are looking for more advanced spatial optimization models.
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I have a hyperion satellite data and I have tried various classification algorithms on it. Object oriented classification has produced the best results in reputed researches.
Hi Gopal,
If you're not set on doing the entire thing in MATLAB there is a paper we have just published which describes a Python based system for object oriented classification which may be suitable:
Clewley, D.; Bunting, P.; Shepherd, J.; Gillingham, S.; Flood, N.; Dymond, J.; Lucas, R.; Armston, J.; Moghaddam, M. A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables. Remote Sensing 2014, 6, 6111-6135.
For segmentation, I'd recommend using only a subset of bands. You could also use OTB (http://www.orfeo-toolbox.org/otb/) to perform the segmentation step. Once you have attributed objets you could export as a text file and import this into MATLAB for the classification or read in as a HDF5 file.
This blog post has some more information on how to install the software (unfortunately you need to run under Linux or OS X at the moment) and some example code:
Hope this helps.
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Please give me your ideas, how social innovation and social validation could contribute to SDI building.
@Karel Charvat good to see you here.
Indeed SDIs have social dimension because at the end of the day these are meant to deliver benefits to social communities. Local knowledge that will come though social groups would help not only to validate the specific needs of an SDI and its rationale for a particular region and but also to fetch innovations from the region itself.
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