BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-europe-2024-academic-tra ck//speaker//HFLJUB BEGIN:VTIMEZONE TZID:EET BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1;UNTIL=20001231T220000Z TZNAME:EET TZOFFSETFROM:+0200 TZOFFSETTO:+0200 END:STANDARD BEGIN:STANDARD DTSTART:20021027T050000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:EET TZOFFSETFROM:+0300 TZOFFSETTO:+0200 END:STANDARD BEGIN:DAYLIGHT DTSTART:20020331T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:EEST TZOFFSETFROM:+0200 TZOFFSETTO:+0300 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-europe-2024-academic-track-MCVY9W@talks.staging.osgeo.or g DTSTART;TZID=EET:20240703T121500 DTEND;TZID=EET:20240703T122000 DESCRIPTION:With the web being a platform that provides lots of features an d a high degree of customizability for creating web maps\, web-based thema tic maps still require expertise to visualize geospatial data in a way tha t highlights spatial differences in an exact and cartographically comprehe nsive way. While most thematic maps show data with seven or less classes\, as determined by (Linfang and Liqiu\, 2014)\, the maker of a thematic map must choose a class count and classify quantitative data to properly conv ey their message through the map. Data classification methods all have adv antages and disadvantages for specific spatial data types\, therefore choo sing the most optimal method is of great importance to minimize informatio n loss (Osaragi\, 2002). Choosing an optimal class count massively helps t he map user to quickly comprehend thematic data and discover relevant spat ial differences. With a plethora of visual variables\, summarized by (Roth \, 2017)\, there are many ways to distinguish classes of features in geovi sualization. For styling features\, mapping libraries provide tools to mak e use of only a few visual variables natively. A thematic map requires a s pecific symbology tailored to the given data\, which distinguishes classes by altering one or more of these visual variables for their symbols. Whil e its symbology needs to be legible and visually separated from the backgr ound map\, it also needs to be created in a way that does not overload the map visually. \n\nThe popular open source web mapping framework Leaflet l acks a straightforward approach to create thematic maps with all basic pri nciples that they should adhere to (data classification\, automatic symbol ogy and legend generation). In the paper\, features and shortcomings of Le aflet in the context of thematic mapping are examined in detail. First\, L eaflet lacks any kind of native data classification process that would be needed to create discrete classes of data for thematic maps. Therefore\, u sing GIS software beforehand to classify and style the dataset properly (t o get class boundaries and exact colours) is inevitable. Second\, for symb ology\, although it makes use of modern web standards like HTML5 and CSS3 to style vector map features (Agafonkin\, 2023)\, it still lacks styling s olutions that are common in traditional thematic cartography (e.g.\, hatch fill patterns)\, as discussed in (Gede\, 2022). As a thematic map require s some kind of explanation of visualized data\, the presence of a descript ive\, well-formed legend with exact symbols for all data classes is non-tr ivial either. Although various tutorials and workarounds are available\, t hose only solve part of the principles. The examples provided by the offic ial website of Leaflet are hard-coded and static\, meaning that they will have to be recreated for each specific thematic map\, making them unsuitab le for implementation in a dynamic data visualization. Moreover\, these wo rkarounds are complex to accomplish\, especially for those who are not fam iliar with programming to an extent to be able to code visually pleasing t hematic maps on websites. \n\nAs a solution\, this paper introduces a high ly customizable\, open source plugin for Leaflet\, developed by the author \, which extends Leaflet’s GeoJSON class and combines all processes requ ired for creating a thematic map in a single step. By combining all the ne cessary processes\, this easy-to-use extension is a solution that wraps th e individual processes of quantitative data classification\, symbology and creation of an appealing legend. The extension puts an emphasis on provid ing numerous options for a highly customizable visualization. It supports well-known data classification methods\, custom and Brewer colour ramps as defined by (Brewer et al.\, 2003)\, symbol colour-\, size- and hatch fill pattern-based distinctions\, HTML legend row templating\, legend class or der\, data manipulation options\, and many other features. For maps with g raduated symbol sizes\, it generates widths between user-adjustable min-ma x sizes. For point features\, the symbol shape can also be changed to pred efined SVG shape symbols. Data manipulation options include normalization by a secondary attribute field in dataset\, rounding generated class bound ary values and visually modifying them by applying division or multiplicat ion (to easily change unit of displayed value). In case the input GeoJSON dataset has features without data for the set attribute field (null/nodata )\, these features are handled to optionally form a separate class with a neutrally styled symbol. Should the map maker wish to ignore these nodata features\, they can be ignored\, therefore not showing up on the map as a distinguished class. As it is an extension of a native L.geoJSON layer\, m ultiple instances of L.dataClassification layers can also still be used wi thin a single Leaflet map object. This allows for more complex thematic ma ps with multiple layers or different kinds of data with a different symbol ogy type at the same time (e.g.\, a combination of a choropleth background map\, with graduated symbol sized points as a second layer in the foregro und). Since a legend is automatically generated and displayed for each ins tance\, they are linked to the respective data layer\, therefore it inheri ts all methods that are called on the layer (e.g.\, if the map maker uses remove() to programmatically remove the layer for some reason\, the relate d legend also reflects these changes). Even though the legend is created w ith a clear and concise style by default\, legend styling can easily be cu stomized with the provided options and CSS definitions. \n\nAs one of the goals\, the plugin facilitates the easy creation of clean thematic maps us ing exclusively open source software and libraries\, with the hope of incr easing the availability\, accessibility and popularity of such thematic ma pping on the web. The extension is still under development\, and is availa ble on GitHub (with examples)\, at https://github.com/balladaniel/leaflet- dataclassification. DTSTAMP:20260601T171935Z LOCATION:Omicum SUMMARY:Beautiful Thematic Maps in Leaflet with Automatic Data Classificati on - Dániel Balla URL:https://talks.staging.osgeo.org/foss4g-europe-2024-academic-track/talk/ MCVY9W/ END:VEVENT END:VCALENDAR