Dynamic Styling For Thematic Mapping

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Dynamic Styling For Thematic Mapping
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Current web standards have facilitated the online production and publication of thematic maps as a useful aid to interpretation of spatial data and decision making. Patterns within the raw data can be highlighted with careful styling choices, which can be defined for online maps using tools such as Styled Layer Descriptor (SLD) XML schema. Dynamic generation of maps and map styles extends their use beyond static publication and into exploration of data which may require multiple styles and visualisations for the same set of data. This paper explores the application of thematic styling options to online data, including mapping services such as Open Geospatial Consortium (OGC)-compliant Web Mapping and Web Feature Services. In order to be relevant for both user-specified and automated styling, a prototype online service was developed to explore the generation of styling schema when given data records plus the required output data type and styling parameters. Style choices were applied on-the-fly and to inform the styling characteristics of non-spatial visualisations. A stand-alone web service to produce styling definitions requires a mechanism, such as a RESTful interface, to specify its own capabilities, accept style parameters, and produce schema. The experiments in this paper are an investigation into the requirements and possibilities for such a system. Styles were applied using point and polygon feature data as well as spatially-contextual records (for example, data that includes postal codes or suburb names but no geographical feature definitions). Functionality was demonstrated by accessing it from an online geovisualisation and analysis system. This exploration was carried out as a proof of concept for generation of a map styling web service that could be used to implement automated or manual design choices.
Axiom of choice Statistical hypothesis testing Presentation of a group State of matter Texture mapping Multiplication sign Frustration Parameter (computer programming) Data analysis Function (mathematics) Mereology Computer font Web 2.0 Web service Different (Kate Ryan album) Visualization (computer graphics) Query language Aerodynamics Information Physical system Presentation of a group Pattern recognition Arm Electric generator Trail Mapping Parameter (computer programming) Process (computing) Phase transition output Pattern language Quicksort Resultant Classical physics Divisor Motion capture Maxima and minima Metadata Number Hypothesis Twitter Population density Software testing Feature space Electronic data processing Graph (mathematics) Information Cellular automaton Polygon Mathematical analysis Interactive television Basis <Mathematik> Computer animation Visualization (computer graphics) Query language Personal digital assistant Function (mathematics)
Polygon Rifling Context awareness Workstation <Musikinstrument> 1 (number) Numbering scheme Client (computing) Parameter (computer programming) Function (mathematics) Web service Testdaten Computer configuration Different (Kate Ryan album) Bus (computing) Extension (kinesiology) Descriptive statistics Physical system Injektivität Electric generator Theory of relativity Mapping Moment (mathematics) Data storage device Parameter (computer programming) Virtualization Bit Variable (mathematics) Category of being Computer configuration Vector space Gotcha <Informatik> Website output Quicksort Whiteboard Row (database) Classical physics Point (geometry) Flock (web browser) Server (computing) Statistics Gauge theory Graph coloring Binary file Metadata Number String (computer science) Energy level Boundary value problem Feature space Computer architecture Form (programming) Context awareness Multiplication Information Polygon Interactive television Mathematical analysis Counting Computer animation Visualization (computer graphics) Personal digital assistant Table (information)
Point (geometry) Probability space Area Arm State of matter Numbering scheme Pivot element Graph coloring Pivot element Number Latent heat Web service Computer animation Visualization (computer graphics) Bit rate Personal digital assistant Calculation Green's function Divergence Right angle Quicksort Resultant Social class
Web page Point (geometry) Presentation of a group Equals sign Outlier Equaliser (mathematics) Numbering scheme Online help Parameter (computer programming) Mereology Binary file Graph coloring Number Attribute grammar Frequency Web service Goodness of fit Bit rate Term (mathematics) Natural number Average Error message Curve Distribution (mathematics) Information Attribute grammar Numbering scheme Single-precision floating-point format Divergence Radius Computer animation Visualization (computer graphics) Personal digital assistant Right angle Quicksort Reading (process) Reverse engineering
Metre Point (geometry) Link (knot theory) Execution unit Graph coloring Machine vision Metadata Number Web service Mathematics Radius Bus (computing) output Pairwise comparison Pairwise comparison Matching (graph theory) Texture mapping Graph (mathematics) Mapping Cellular automaton Electronic mailing list Cartesian coordinate system Sphere Distance Radius Visualization (computer graphics) Personal digital assistant Buffer solution Right angle Quicksort
Table (information) Mapping Link (knot theory) Outlier Numbering scheme Graph coloring Dimensional analysis Field (computer science) Attribute grammar Shooting method Arithmetic mean Computer animation Feature space Table (information) Form (programming)
Domain name Axiom of choice Group action Presentation of a group Electric generator Mapping Multiplication sign Set (mathematics) Information privacy Rule of inference Computer animation Forest Network topology Emoticon Normal (geometry) Quicksort Game theory Resultant Sinc function
Slide rule Presentation of a group Computer animation Visualization (computer graphics) Multiplication sign Mixed reality Pairwise comparison Resultant
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the so mind some Moncrief among the presenting a paper did or the presentation based on paper did on and on in dynamic styling full-semester nothing is a paper that UK goal and I heard him for this so and is clear things moving more toward data exploration now so and we have to do it but in 2 more data science and GIS is the same moving away so this is being enabled by automated data processing and this enables hypothesis testing for example you can interact through from derived outputs of the datasets have been explored from visually and do both hypothesis generation by being exploring an aspect and then what's this testing my of visualizing in a different way and so now I'm interested in is non the had enable this and to do you have enabled us dynamic styling In this sort of construct so and this was a clear that we need a united to adopt the user driven approach to web GIS so what's so the user inputs a query and queries essentially everything and then make is a flexible and approach rather than a supply push so you publish result this is more of a user pull so you I let the user derive the result on the fly for themselves based on the questionnaire interested and so the of the eventual aim is to develop a web service for this systematic visualization so the idea is you have a piece of data the he goes to the web service with metadata tags on parameters on had a solid and in generates visualization and so this state is really just an exploration of methods of functionally parameters different ways to answer different questions of of some data so as the data exploration and intercom interest in the presentation of that and so we have a data access through WFS another means nearest queries and we're WPS wasn't using that data interpretation so this a very crude driven by you input dataset of a complex datasets you process it to produce an output arm and then develop final step is a day presentation had you presenters output as this is some thematic styling so there are a couple ways to book a couple phases like in a different part of Don semantic Starling 1 is you can use it to presenter results so in this case your publishing results derived from data so I was gonna say about population density that sort of thing around and this is a static results so this is sort of you can apply a known starting technique it can be dynamic style including unique and factors that body and that sort of thing that the result is a system known as the next 1 is 1 months over interactive data so you really try represent data to a user rather than it is on the right result that on someone else's used so you know let the user answer the question for cells and to do this a basis for more dynamic because well that the data is processed on the fly and she a virtual light and so what happens there is you don't know the final letter try so you have to produce so you have to give virtual methods for the user to be up to store all this data presentation and that flexibility is crucial because as I said we don't know what questions use can also of the data but we wanted near enable them to interact with the data and so it decide by them this idea a gift for thing driving this is information visualization so this is the interactively interactively are presenting data visually and so in enabling the user to explore and identify patterns within the data alone and part of this is to make it very young dynamic and to to try ambition capture late underlying trends within the data it some time come very there's unaccusative pattern recognition and so some of these trends can become given the right visualization of very evidence so the outlier detection when you look at a graph and you can see the outline and so in spatial data and the systematic maps for example a copper so if the and polygons in each polygon is labeled according to what is colored uh according to the value within that and of derived using them map classication technique so a method to adjust to partition the feature space of the polygons and then the choice to college represent of the value the net polygons and so this also a number of methods that we can use that what's the right 1 so is it should be at specific method to determine that frustration the coloring another stalling on factors or is it a question-specific so mu what question users asking and the things she so both in depends on the data depends on the situation so and then the other 1 don't induces up again so you have the visualization process is to extract data and then you render um when you're doing so analysis you in presenting the data you can actually drive multiple results from this dataset answer features all you can also use mostly visualization there is this a flexibility because the said you don't know the pattern and data before
and and so you want these you have to tease out you have to give them the flexibility to the fuse multiple ways to try and find the information they're looking for and here we produce from the most recent census a normal and so just a quick signed to and that that's the only the map station and it's this installed to stop description of stolid description GeoServer making an XML which defines uh how we get a value and polygonal point should be drawn the color of the thing so you do this we classify the feature space into in discrete categories by using different criteria so as an equal tools that to raise social some of that stationary and then answer and I so sell really good for this I and you can just make available through a web service it should becomes this must map classification where it returns on the the bins McAllister been in the form of and that sort of thing and for color schemes are adopted the color which I answered derived from matplotlib so if you like we derive that in values in this color scheme automatically or even the store 256 and databases that she's pick the ones you want others of advantages both that and then the other books and so where's this style descriptive generated so they can generate on the server so down just over the years it generate systolic descriptor and w mess you can specify start descriptor and also that this a stolid descriptor service so you give us some data in in you that style and then then it cannot be done generates on the client so the user selects a station color and the climate changes so this is a very broad level of the base architecture that required to do this sort of thing so you have the site which is essentially the star based on the local extent of of the extensive amount of users looking at 40 you don't star based on all the data in a dataset and the analysis method which is in a station and then a color scheme and so this goes to and that's a classication is determined and based on the number of bins the color is determined and then the data vectors the input with the feature space now that they that can be anything from WFS service and the repair service that sort of thing is is the idea is to make a very restful wish and then if you can for of vectors that's on top of that and wanna the parameters on the visit a lot and this is an exhaustive so you can the styling attrib u active so what attributes In this forms of virtual as created on the fly should be used to run crater seen so if a polygon you can add the boundary any color thickness so the thing that has to be that and this can be done on the client says is less crucial and you provide a label is is that can interfere with the visualization of point options and that's a bit funny economics wire radius Ethernet milady like and search marking differ and it can be linked to different variables with the dataset and then it can also be a distant relatives and that idea to the label not so 1 label provide context but colors more gets intuitive and then the opacity and board of so think so is resent a whole bunch of different visualisations of some data that are of the with the last year or so and so 1 is health dataset which was 11 million half ization records and on marketplace or statistics from using Adobe BPS and then enzymatic map is the output from and sensor data so engaged data for example rainfall asserted so the health data is spatially contextual so it's a spatial context is applied to the records for accounting is not technically a spatial data set so that the weighted you it is very much a polygon and summarize values within polygons the sensor data was point data so on the case that a rifle gauge string and so cages and board data flock of of the gages profit forfeited and then the final I wanna look there is a service data so I shouldn't have become bus stops writing the public transport services that this can be applied to and health possible so that sort of thing so on the basic interaction Univision essentially is that eventually out the data is input and then there is no you can specify style latent the parameters is you extra metadata the use of moments that the system so far injects the metadata to determine the style and then some articles come from the user but are question is what do I need to supply user for them to theme that data so the the way to make sense so and then this also sold them style either favored the I guess Jason follows importing injects the color each a polygon points or other and then that can be viewed as a charter table or a map on the client side of island can use essentially w mass and solve thematically and then that just slick American and then the final 1 is the star the scripture itself the sounds of ending with the number of count for being close to the user the so summary of the datasets
so the first one is essentially the calculation of the probability of access to services it for granted so it's not you actor but it is essentially showing from within a region this is the probability of the people having access to right so a so the 1 on the left is stored using equal intervals with 10 intervals so it's and probably the answer and the point 1 that sort of thing has a very nicely competitions a probability space intuitive numbers and the 1 on the right is more geared toward sergeant specific question so in this case if you're aiming to provide at least 75 % coverage to region of probability of point of 5 I you can use as a pivot point and then have a divergent color scheme around that so very quickly you can see red sellers there is need work wise about right green is so that 1 is more answering a specific question for I guess my aim is to have 75 % of population covered where is and so that that's visualization very much I I guess was that the probabilistic 1 the equal intervals is more like a so this is my of distribution of between 0 and 1 I guess
that this 1 is the the right ratio so what it is is a disease of problems right compared to another race in this case what each of area so called yes there is essentially no census Arab armies the rate is calculated for the area and the rates calculated for the dividends are tastelessness roaches were left arm and compared to that so red is essentially that the rate for the region is higher than the normal rate in the state the green is slower and what is it about right arm but because of the way this 1 is calculated really only makes sense to visualize it as a divergent color scheme because it's essentially 3 classes and so 0 1 and 2 and so this on divides the 3 classes of and why it makes sense so give the user the ability to and change the color scheme does make sense because this is since this is a data-driven as a result driven from the data and visualizations so you on chances to much 1 on the other hand this is a
disease prevalence rate as so this is the error rate of disease a curve that person as smooth the 1 on the left again is done using equal intervals and because of our allies equal intervals has a tendency to sort of blanket everything within a few bins and then have large gaps and then you might have 1 so that the end so and it's and 1 right quintiles that it divides into 5 of 5 equal a number of bins and so the 1 on the right shows some far better spread all of the you know what diseases occurring where the 1 on the left is very good fella detection so that your questions from the outliers add that'll work but it it's show me the distribution of the disease prevalence of the 1 on the right is a better way of showing that distribution a Prof McAllister that's completely wrong and because green is the highest prevalence of the disease right which intuitively makes no sense because you want red so in this case and so this is a new 1 of the parameters you have to have a reverse for a new color scheme so you know is high bad hike you'd all in terms of coverage tries scared like I referred service was tensor disease it's not so I and then the other part is will this really should be their red yellow green or green yellow red and white but with a red shown in bad this and how we interpret stuff means gets a low rates period are red is bad so I write that so while I shows that spread the color schemes doesn't work the use of looking nature ugliness find advantageous to look at using uh well not so much and so there are some things to account for his the as a a
giving and they use a lot of leverage sometimes doesn't help so in this 1 of us is our and baseline page data so it's 40 years of and daily readings um and so the way is generally visualize out wrote ways generally and summarized is a short term of a long term 1 8 is greater or less and that's the thing so the 1 on the left shows how will you can sort of compare around points bodies the 1 on the right there is again divergent based on Wall 1 he's 1 is equal to long term and short and the long-term average as so that actually is better for comparing within a sensory so you know the really you can for these was to compare against each other or compare with an and you can also show multiple use of up but essentially this 1 shows that the radius is based on 1 attribute the QoS-based with another and the label is based and also we people 3 attributes on there and this is more of a presentation a contextual on visualization because you putting too much maybe information and therefore yes the silent intervals so if you wanna publish this this may be a good 1 but if you want to sort of intuitive and maybe less this
is the radius roe thing to ground so this is the service coverage so and this is the sort each of the color represents the number of the units of people who have access to that best and so what I did here is the radius is actually 500 meters is happening and made a buffer because that's how the them they can't the coverage so you can actually visualize the radius the use of about on the map and so see whether coverage is what the full house find in the spheres of things another way to do it is the size of a represents the coverage of size and color in this case on match and the right here is ellipse which the EU y is the number of houses covered the x is the number of services the bus stop and the colors is again the same as the y and this 1 this is quite useful you policy which is actually good but if you had an elongated X which is a large numbers of so get lists of basal that's I you have a lot of services for nominee people public so this is a very not nice way it into that's OK and then we go change at once all y all recurring so much and this 1 is on this if you can inject the Starlight descriptor we're in the metadata so this is a vision agreed applied to summarize the point of sensor data within each grid cell and they have a very particular way to interpret that so that that can be injected but what else to do with this 1 is on calling it an MS alliance so in the metadata I put the points in a liar so I just all of the same so that's 1 layer with the the decreed in the points used to derive and the should match layers going work the keep out I can't link visualizations and again because linked and this 1
the left is a sort of a mile disease right from map is the In disease right and so the the axis is the left map and the Y axis is right mapping again that link by color and can make the mark of and lots of things on is just a quick way to so do spatial comparison but then you also have the Congress graph do comparisons that which leads me to this and
some of the data I'm looking at art technically or uh traditionally GIS fields so epidemiologist like tables but that doesn't mean we can't put the cartographic solving inside the table and maybe solvent and shooting that color scheme means this and so when they see the map they can serve make that link and then again you can do them multiple and dimensions so this this 1 is these rights and with this the cartographic selling applied it a bunch of on the different attributes so you can use the multiple dimensional analysis based on that and
again you can show Muffled physicians so this is the same as what we saw before and this is using a form of this is showing the feature space so this is the way showing the allies In this lady to analyze there 3 to show the outliers while showing a sensible and styling method as well so you don't so you get those 2 pieces of fracture 1 genetic do equal and tools and then something should give you a better spread of the disease and the last 1
is with on essentially so the sad emoticon tension there's no result here so on thinking that essentially we should be able to just like I'm normal maps the tree represents a forest we should be have iconography which represents something within that so the can't generate result I have 1 realizes this the that sad face new result is India privacy this action most come with a metal plate good enough that sort of thing and the good
enough time the the the all of on tour of the house and game theory gives too far you know constant and can do what if I may the mark by leans 0 yeah presentation because many jails so TOAs give us so many choices but there are no if philosophy or the agony of Keegstra is all that is a layer or what is a right and wrong and I think so you have given and he gave us real what is to be concealed and the and all of that altering the on this this is more me trying to figure out engaging by users of reactions so you can still sometimes I slide up popped up that's the 1 teacher in that 1 and other times people like both and then other people out 1 of the other so the idea is to be flexible but then how flexible other Christian and also we have you get a use it this year it I'm a I'm really asking this question you can't say put that into a form since after this and a general sense so you have absolutely is in important that as but to your presentation I wonder you know ensure the domain set can be dividida or expressed up to the rule of 3 D brain region to court and it should go to
the men the men released to the general here and I had the best of times so you can put the too low doses and mix it together right so you apply you had yet there still and have it at at all the Europe was of presentation slide what happens when the data on the some result on the visualization and this 1 is 1 dataset but then I felt 1 male female so earlier and these 2 results in and then does this especially tialization and the status of the again and the thematic starting is of the adopted in all visualization OK good a probe into their mentions ever bet is the presentation
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