Geographical Analysis - Science topic

Waiting for the answer

Yes - and a specific example is, for instance, how disabilities of various sorts are expressed as the volumes a body might occupy while stationary and in motion, and other volumes and geometries of the the senses ( or lack of ) when intersected against the environment from furniture to the room, building, sidewalks, to transit stops, the vehicles, at origin and destination - and with other more ordinary human bodies and guide animals. It could also apply to disaster and emergency response, where where first responders need to traverse sometimes miles of rubble and move stretchers around staircases in nearly collapsed buildings. Christopher Alexander's "A Pattern Language" ( https://www.patternlanguage.com/ ) superficially seems to be about architecture, but reads more like human scale geography (space, time, and people). Much our attention in day to life, to some degree is determining our our body might fit in a space or move through it, at all scales. Even our thinking and language is founded on spatial metaphors of embodiment - George Lakoff, "Women, Fire and Dangerous Things" ( What Categories Reveal about the Mind ) "... conceptual metaphors are grounded in embodied representations; that is to say, we use sensory-motor experience to conceptualize abstract domains such as time, feelings, interpersonal relationships, etc. .... Thus, given the prominence of space in our perceptual and motor experience, spatial dimensions are frequently used to support rich metaphorical conceptual systems ..."

It a domain of geography that is at the intersection of many different fields ( Hmmm ... like most geographic thought). Anatomy since ancient times has used geographical terms to describe the human body, and now GIS analysis helps bring together layers of spatial geometries and attributes, "Geographical Information System Applied to a Biological System: Pelvic Girdle Ontogeny as a Morphoscape".

Gbenga Olayanju hello, sorry for the late reply

In my case of study, this problem mostly arose from the variables with low correlation to the dependent variable. Plus, it might be due to the local modeling allowing heterogeneity to be explicitly modeled, since there are varied regions inside the sample.

However, I don't see how a trimmed mean would work since most of my independent variables are binary or low count variables. Plus, what tools do you mean by advanced regression analysis? Could you cite the papers you have in mind?

Fruit trees may be identified with more higher resolution rather than at 25 cm. However, in your case, First you classify the trees and mask them out from remaining part of image using supervised image classification. afterwards, you may reclassify the image using programming language like R and Python. It should be noted that at 25 cm resolution should be classified. If you can elaborate your outputs here, will be good to understand problem.

I follow this topic.

The OPGD model and "GD" R software package were recommended to identify spatial determinants from a perspective of spatial heterogeneity. You can refer the guide to use the model https://cran.r-project.org/web/packages/GD/vignettes/GD.html. As a result, you can visualise contributions of determinants, and the interactive impacts of spatial variables.

Very good question. Please have a look at these:

ELUM: A spatial modelling tool to predict soil greenhouse gas changes from land conversion to bioenergy in the UK.Environmental Modelling & Software ( 2016).

84 : 458-466.

Highlights :ELUM models soil greenhouse gas balance of bioenergy land-use change in UK to 2050. It is based on the ECOSSE model, but quick and easy to use, with added features. It is able to support life-cycle assessments and policy making for bioenergy. Consultation with anticipated users guided usability and functionality. Greenhouse gas balance is highly dependent on initial land use and new energy crop.

Mountain ecosystems are geographically very unique and the changes in plant populations of such ecosystems are need to be carefully monitored. In the scenario of climate change and global warming, genetic diversity is much essential to adapt the plant populations to the changing conditions.Remote sensing ( aerial photography)is one of the useful method to assess the fragmentation of plant population in mountain ecosystems.

A nicely maintained list is here:

List of freely accessible LiDAR data and digital terrain models: https://arheologijaslovenija.blogspot.com/p/blog-page_81.html

Hi!! There are many ways to do that, if you do not have many polygons, you can do it with a simple clip and the calculation of the areas in attribute table. Then with the area of the complete and clipped polygon you can calculate the percentage of overlap. If you have many polygons, there are many functions to explore as those recommended the colleagues above. Depending on the software you use, you should google overlay polygons ArcGis, or QGIS, or R..

To the best of my knowledge, GWR or MGWR accounts for spatial dependency as well as spatial heterogeneity. From Fotheringham et al. (2017) "... Hence, not only does GWR identify spatial heterogeneity in processes but it also takes advantage of the spatial dependence in data—so tying together the two main distinguishing features of spatial analysis."

Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.

We cannot all face one direction. If you need Mr A for medical advice, am sure Mr A will need you when he feels like traveling round the world or when at his leisure time.

Google Maps has the most reliable and accurate database for geocoding (assigning lat lon coordinates to addresses), here are instructions for their API: https://developers.google.com/maps/documentation/geocoding/intro

If you're familiar with QGIS (https://www.qgis.org/en/site/forusers/download.html), you can install plugin called MMQGIS and easily geocode list of locations in CSV format, here are instructions how to download and use the plugin: https://www.lib.uwo.ca/madgic/projects/gis/Geocoding%20Using%20QGIS.pdf

You should prefer using Google as a source rather than OpenStreetMap (OSM).

It is not in QGIS but in ArcGIS tool Count overlapping polygons can be useful.

we used it mainly with biological invasions. The important thing is that You have a database of georeferentiated data. If so we can also discuss it together

Thanks.  Theoretically you could download SRTM (Shuttle Radar Topography Data) and calculate the slopes from that (I would use GMT - Generic Mapping Tools grdgradient + grd2xyz, then sum z column for that,  but I presume arcGIS or similar offer similar functionality - the trickiest part will be to fillter out those values that belong to Europe, or to EU.  But I doubt this is the right approach - the average slope will be heavily skewed by mountaineous regions which will be just the regions without agricultural activities.  So you would somehow need to also include information on land use (e.g. using CORINE database on land use). You can see it gets complicated and really this would require a chunk of solid research.  I seriously doubt you will be able plug some number you found on the internet or a ResearchGate colleague might give you on this thread into some formula and expect to get a meaningful answer (I should point out that I am not a geographer, though)

QGIS (http://www.qgis.org/zh_CN/site/) is the most commonly used free open source software for analysis and visualization of spatial data. It is user friendly, well-documented, equipped with hundreds of tools, and enables implementation of user's algorithms.

SAGA GIS (https://sourceforge.net/projects/saga-gis/) and GRASS GIS (https://grass.osgeo.org/) are also worth to consider. They can be installed as standalone, but they are additionally integrated in QGIS.

Dear Ron:

Thanks for your rapid answer.  If you agree, I'll send you privately the list of species I found in TAXREF v10. For each, I'll add my estimate of the probability they really exist in La Reunion based on their geographic distribution in the Catalog of Fishes online.

Thanks again for the good work your are doing for all of us.

Best regards

Dom

Hi!

Here is a list of links with instructions on how to use GWR on raster data in different software environments:

QGIS-GRASS: 

ESRI ArcGIS: 

GWR function in R

GWR python library:

Hope this helps you get started with untangling your task :)

Good Luck!

Best regards,

Karolina

Hy Ramana you can find a lot information of India here:

Best Regards

We have addressed some of these questions in the past, and basically have to come up with some refined criteria for habitat, such as when is a low density forest with savanna understory a non-forest, and when does it function more like a forest such as a woodland with still some grassy or forb understory.  Also how small should the polygons go to be considered either forest or non-forest, how big should an inclusion be such as a small patch of trees in a savanna, is still functioning as a savanna, and a small opening in a forest is still forest.  I am unsure of a tull technical agreement, but such as a low density forest of 10-20 BA(basal area)/acre of trees with savanna understory is still considered grassland for habitat purposes.  Woodlands commonly 40 BA/acre of trees even with grassland understory from perhaps burning is often considered forest.  These kinds of decisions are typically best resolved by discussion or agreement with wildlife biologists, ecologists and foresters or silviculturalists, and may vary with the circumstances and importance of specific habitats and fragmentation to species involved.  Also you  might want to look at type of road, whether it is open for full time use, or a temporary or intermittent road, open seasonally or just for specific activities at some low frequency, but heavily grassed and relatively narrow in width, and closed to traffic uses and disturbances most of the time.  And it never hurts to analyze the problem with one set of criteria, and then reanalyze with one or more other sets to make a slightly different point relative to the situation.  In GIS, you can set and describe polygons of tree density or habitats and roads to perfect its utility to your subject, which probably has to do with habitat fragmentation.  The difficulty as your question suggests in how to best look at road fragmentation to a forest dominated landscape with mixture of non-forest.  The approaches you use will determine how best to accomplish.  If small inclusions of forest or non-forest of a select size are to be combined with the dominant land cover type, then that would be your first step, then decide if you want to evaluate any road or just certain types of roads, and whether skid trails or landings of timber that are still barren should or should not be included as a disturbance of habitat.

Hi, I found some useful articles on principles and related strategies draw upon transit-oriented development (TOD) concepts. Hope they are of useful to your study.

Thanks for the suggestions guys. I discovered it was a TWI layer I produced that was causing the problem. It was in a floating point pixel format which I have changed to signed 32 bit, the same as my other layers, and this seems to have done the trick.

here is the beautiful link where the solution to your beautiful problem has been discussed.

here is also information

here is the video which fix this problem

Thank you for your response, it's really helpful.

Tony,

Not quite sure exactly what you are looking for. Are you wanting data like location, latitude, longitude? Like the link below

There is a Tool called Minimum Bounding Geometry. I don't know if it will offer you the ready data that you need but is very flexible and you can work things around. 

Dear Victor,

from what you wrote and please correct if I am wrong, you have to use an equal-area projection. That means that you have to use a measure-preserving dynamical system.

Examples of this kind of projections are: the Lambert cylindrical, Behrmann, Bonne, Mollweide etc.

Dear Munthali,

Here are some resources for work done regarding LULC changes using ArcGIS, these might be useful to you.

Best wishes,

P

Hi Tofigh,  I am not an expert in this process, but doing spatial analysis all comes down to similar problems-missing data. Would using the average of both controls work?

Thanks  so much Tiago. I only want to get one site's GPP (e.g., N 47.7790

E 107.7980), I can not find it from the website you provided. BTW, Can I get the data from 2014 until recent, like october, 2015?

hi

yes why not

Hossam

The first mathematical formulation of urban land use may be attributed to:

Johann Heinrich von Thünen for his (1842) formulation of land rents and the idealization of urban-agricultural regions in concentric circles. 

For a modern revisit of von Thünen's formulation:

Dear Milan,

Are you looking for crop marks? There is a great number of publications in archaeological literature; the methods can be used partly for modern features as well. See e.g. Lasaponara & Masini 2007 (via RG), you may want to check out Agapiou et al.  2013 (open access). I got interesting results with entering this search term up in the RG search box.

We were also successful using archive aerial photos made in the 1950s (at that time the ploughing depth was smaller, so e.g. the Roman archaeological objects were not yet destroyed).

I am sorry if I misunderstood the question.

Kind regards, Balázs

Have you tried Landsat at 30m, you could extract water bodies using object based classification in SAGA GIS open source software, as an indicator of water body disctribution (or lack of water bodies). Sorry that is all I can think of.  

The resolution of a DEM commonly refers to its horizontal (x-y) resolution, meaning a 25m-resolution DEM has a real pixel size of 25x25m, where one elevation value is assigned to each pixel. There is no direct relation to the contour intervals used to derive the DEM (that would refer to the DEM's vertical resolution, which is rarely provided).

You can also try other indices too. Here is a good collection of them with explanations - www.indexdatabase.de

Hello Yifan

You can try with the script developed by Babak Naimi, available in http://r-gis.net/?q=ModisDownload

The ModisDownload function allows to mosaic all images from the same date. In addition, you can also reproject the images at the same time.

Have you tried the Global Self-consistent, Hierarchical, High-resolution Geography Database?

Dear Subir,

Oro- is an element, from the greek, that makes up names with meaning of 'mountain'.

Orotemperate is a thermotype from temperate climates.

Thermotypes clasification are: infra-, thermo-, meso-, supra-, oro-, cryo-,

Have a happy New Year.

Hi. Converting raster map (jpeg) into vector layer is quite a problem (it takes time, unless it can be done automatically, but it is usualy not the case). But it seems it is not necessary in Your case. Just do the georeferencing of the raster map and You will be able to read the coordinates. If there are no points of known position (even cartographic grid will work if you know the projection) on the map (georeferencing is impossible in that case), find some already existing shapefile of the area (e.g. a coastline) and try to fit your raster map to the one you have found. Good luck.

You are looking for areas of endemism as these are areas with species with the highest co-occurrence. So there are a lot of methods, PAE, NDM, nested clade-area, etc. Among these I suggest the NDM it is the best to be a spatially explicit method. I had a paper accepted (and should be published shortly in PlosOne) in which I propose a new method for identification of areas of endemism that does not use grid cells (like the above methods).

Thank you so much,everybody for your comments.

Thank you for all your answers! Could I use flow velocity to limit the time steps for every grid?  If this is workable, I should to confirm the flow velocity for every grid. Is there a calculating equation for flow velocity for every grid both considering the  weather (rainfall volume, intensity, and duration) and land surface (spatial scale, slope, landuse, and soil) conditions?  

You can estimate biomass by using dimensional parameters, i.e., canopy cover, length and width of diameters as has been done by Gholami Baghi et al. (2013). You can download the full pdf , Journal of Rangeland Science, 2013, Vol. 3, No. 4 Gholami Baghi et al.

You can use remote sensing techniques to get information on aboveground biomass and make measurements on large scales. For details see Mundava et al (2014). 

Remote sensing techniques have been used by Porter et al. (2014). They have developed regression models for estimating biomass yields using data from satellite and ground based remote sensing platforms in pasturelands of Montana.

You are right. Toposheets are used to prepare DEM and its derivatives required for watershed development. One more use is to georeference the area.

It is not clear, what are "residual magnetic properties" which you want to maintain. For what purpose and how long do you intend to maintain this property?

Of course, there are magnetic properties which depend on size, volume, prehistory of sample etc. Some magnetic properties can be changed if we replace objects surrounding the sample or if we measure magnetic properties applying external magnetic field.

If elemental, phase and mineral composition of the sample almost the same as had the original rock, you can measure specific properties of sample material, like susceptibility, permeability, saturation magnetization, coercive force, residual magnetization, taking into account effects of size, shape... Statistics of results of sufficient number of samples also could help to understand properties of the original rock. 

Your question is indeed a bit too general to answer.

It is a topographical map I guess.... in such a case I would advise you to digitize it and from ArcGIS or any other open-source software like QGIS, you could define the channel slope and then the cross-section calculation necessary + knowing what is at the bottom of your chenal (or should be) for the roughness parameters....

The real question comes as Kazimierz said from the map scale... as I guess you will want to do comparison with present-day discharge.... and you will need to make sure that you pick the coarsest scale as a reference scale for the different measurements.

J'espere que c'est ce que tu voulais!

Thank you, I later found that out and bought WMS.

Thanks Pino Cosentino. It was quite easy with  saga . 

Thisal,

Your Question(s) are a bit ambiguous to me. Here is the justification:

1 - In the title you asked: What are the methods of measuring the LENGTH of a statistical self-similar fractal? So the unknown is the LENGTH of the fractal boundary.

2 - in the body of the question you asked: Is it effective to use results of measuring length of a statistical self-similar fractals...for measuring geographical boundaries?

So this is a completely different question.

For your convenience, I am going to answer both questions:

1 - To measure the length of a deterministic fractal boundary (or a curve in general), we need to set the smallest possible measure on our scale and add the corresponding measures of all the disjoint sets forming the fractal curve. I.e. we are applying a direct property of the measure theory which is: Measure (disjoint sets) = Sum of the measure of all sets.

2 - Since the geophysical boundaries are somehow "stochastic" in their structure, It is tough to apply the methods of the measurements of the deterministic fractal (particularly if you cannot determine the Hausdorff dimension). As a result, It is not a rigorous method to estimate the length of the physical boundaries using the technique of part (1).   

Hope my answers satisfy your inquiries!

Hello Malleswararao, 

I'm sorry I didn't answer your question earlier... It seems like you didn't have the netCDF libraries installed, or if they are in your computer, the CDO can't find them, since you're trying to work with a .nc file (the extension for netcdf files). 

You can find the netcdf package here: http://www.unidata.ucar.edu/software/netcdf/

Perhaps, you will need to recompile the CDO with netcdf support enabled. 

Good luck & regards!

you can download draftsight from http://www.3ds.com/products-services/draftsight/download-draftsight/.

You can transform the arcs in polylines and in the list of properties to get the corresponding lengths:

http://www.eng-tips.com/viewthread.cfm?qid=329749.

+

You can generate the surface model showing the elevation. Overlay with digital GIS layers of water bodies including, river, ponds and lakes. For the area demarcation regarding arable land you may choose to prepare the slope map and slope direction. An overlay map can be generated to find the suitable arable map, deducting the slopes not suitable for cultivation. Use overlay techniques and query tools to work on the raster maps. If you are using ArcGIS this would be feasible.

I just discussed this with a colleague, and we both agree that the use of the term "scene" is sometime misleading. However, the scene doesn't refer to an exact location. For sure, a scene has a size which is usually expressed in km at ground level, while an image size is expressed in pixel. Then why isn't a "scene" referring to a given "map sheet" ? Probably because there is no exact coordinate matching from a scene to another, even with Landsat. Landsat is very convenient since you usually get very close match within some classes of images. But there are still some differences from one image to another, especially if you perform an analysis over a long period of time : corner coordinate mismatch, row and column count mismatch, resolution mismatch (30m pixel was actually 28.5m), etc...

In the "NASA’s Global Orthorectified Landsat Data Set" (http://glcf.umd.edu/library/guide/PERSMarch_04_313-322.pdf), one can read "Data at these spatial resolutions can provide a high potential mapping accuracy of natural vegetation and alterations to it, if and only if highly accurate scene-to-scene within- and among-date registration is achieved."

Another thing : here, we more and more perform studies with mixed products, combining different sensors. Do you think it is relevant to re-define this term for Landsat-only studies ? Is it really still so informative to refer to a "scene" as being the sensor-specific covered area in this context ?

Anyway, very interesting question. This is not really an answer, just my two cents...

I've worked with geographical datasets for many years and have worked on similar problems. It helps a little to think of the problem as a nearest neighbor problem in 2D space. That brings together possible matches, but the crux of the problem is how to measure the similarity between two geographic features, that is, how to compute a distance function for each pair. For text similarity, there is the Levenshtein edit distance. I have usually improved on the basic algorithm. For comparing the geometry of 2 features, I have used dynamic programming algorithms. Levenshtein edit distance is itself computed by a dynamic programming algorithm, and the algorithm I have used for geometries is somewhat analogous. But those only deal with some of the complexity of integrating two geographic datasets. It is not simple.

Well, a point is a very approximative representation of a landslide and will have an uncertainty, that is correct. If you have an idea about the mean size of the landslide bodies you could define the uncertainties of the landslides location represented by a point. Then it is appropriate to generate a buffer around the landslide points according to the uncertainty ( e.g. 20-50 m). After you can use the remaining area (ouside the buffers) for random sampling.Otherwise, even if you have no information of the landslide size you could still try the buffering, but then the buffer distance should be selected carefully. So you would most likely avoid to have landslide points in a non-landslide sample.

But a I have also a question, why are you using points to charcterize landslides from aerial images? What kind of images do you use (in which ground resolution)? What is the expected scale of analysis? Since you generate a sample for non-landslides points I guess you're giong to do a statistical analysis, logistic regression?

I'm asking because, if the resolution of the images allows the direct mapping of the landlide extent I would use polygons instead of points to do that. For high resolution images even a separation into source and deposition areas is recomended, since after you could gain additional information via magnitude-frequency statistics.

Best Regards,

Jewgenij

Do you have the metal concentration map?

Hello,

Maybe this link will be useful for you:

http://serc.carleton.edu/eet/measure_sat2/part_1.html

Good luck!