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Developing FOSS4G Walkable Living Area Planning to Aid Korean 15-Minute City

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Developing FOSS4G Walkable Living Area Planning to Aid Korean 15-Minute City
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The concept of 15-minute cities, aiming for accessible amenities within a short walk, has gained traction. In Korea, Chrono-Urbanism supports walkable neighborhood planning where essential services align with residents' living spaces. By evaluating walkability, bike accessibility, and transit, this study develops a FOSS4G-based tool for urban amenity distribution. The tool integrates multiple accessibility aspects to determine optimal urban amenity locations, using a network-based approach to minimize travel costs. It's designed using QGIS and Python, considering resident population, existing amenities, and urban environment. The tool's application assists officials, planners, and researchers in 15-minute city projects, identifying amenity needs, enhancing walkability, and aiding sustainable urban development. By leveraging FOSS4G, this tool promotes data-driven amenity placement, encourages sustainable transportation, and contributes to equitable urban development. It aligns with the 15-minute city vision, enhancing citizens' quality of life and fostering transparency and collaboration between planners and the public.
AreaLastteilungModule (mathematics)PlanningPoint cloudOpen setSoftwareSource codeFreewareService (economics)Greatest elementComputer networkProcess (computing)Group actionTransportation theory (mathematics)Neighbourhood (graph theory)MeasurementDistribution (mathematics)Open sourceAnalytic setSpacetimeInclusion mapComputer multitaskingAmicable numbersPersonal digital assistantTable (information)HierarchyTime zonePhysical systemDatabaseCharacteristic polynomialLocal GroupFrequencyCondition numberPredicate transformer semanticsCase moddingRaw image formatNavigationCompilation albumScale (map)Square numberMilitary baseIntegrated development environmentVariable (mathematics)Type theoryMaß <Mathematik>Convex hullPerformance appraisalFeasibility studyCoefficient of determinationMathematical analysisPrice indexMathematicsGamma functionPlastikkarteRAIDData typeFaculty (division)PolygonBoundary value problemBuildingAddress spaceLink (knot theory)Execution unitStandard deviationGoogle Street ViewComplex (psychology)Pressure volume diagramElectronic visual displayInformationFunction (mathematics)EmailAsynchronous Transfer ModeFinitary relationThermal expansionGenderValue-added networkData managementConfiguration spaceLibrary (computing)Limit (category theory)Data structureObservational studyArchitectureStatisticsMixed realityAdditionPlanningGroup actionDistanceStandard deviationOpen sourceProjective planeLocal ringDatabaseAmenable groupAreaModule (mathematics)Transportation theory (mathematics)Physical systemSoftwareExecution unitBoundary value problemPairwise comparisonCore dumpEndliche ModelltheorieObservational studyCategory of beingGenderMathematical analysisTerm (mathematics)Library (computing)Netzwerkdatenbanksystem2 (number)PlastikkarteLevel (video gaming)Open setCASE <Informatik>Process (computing)Operating systemPresentation of a groupNP-hardConnectivity (graph theory)Neighbourhood (graph theory)Computer animation
Computer networkComputer-generated imageryArchitectureAreaObservational studyMathematical analysisLibrary (computing)AdditionStatisticsMixed realityPersonal digital assistantSpacetimePoint cloudComputer animation
Transcript: English(auto-generated)
Thank you. I'm from Seoul Institute of Technology. My presentation is designing a workable living area planning module to assist the Korean
50-minute city. This is very hard topic for planning, but it is some challenging issue to applying post-poetry technology into the planning support system. It is a conceptual model, so it is not an operational system, and we are ongoing to
make this system. This study is five years old, and this is the second year conceptual model design staging.
The Korean urban situation is that we are the population issues, so our planning is to applying some urban amenities, urban facilities to workable within some 15 or 20-minute neighbourhood
walking distance. So this project is applying those concepts to this project, and our planning support system for 50 minutes, Korean 50-minute city is two pillars.
One is designating the workable living area planning boundaries, and the other pillar is locating the urban amenities.
And our goal is using some network-based analysis module to this planning support system. So we compared some other cities, comparative cities to applying this concept, and our
similar approach is using the OSM model, but it's not the OSM model because we have our own urban database, so we based on the PDSTRE network database to analyze the workable
area and reallocating the urban facilities or urban amenities. So first pillar is to categorizing the workable living area, and it is based on the big
data. In Korea, big data is a kind of data from the smart card data or the mobile phone data,
but it is very common, so also it is commonly used in urban planning, so we're using the data and categorizing the workable living area. And the other pillar, distributing urban amenities, it is based on not just the walking distance
but also applying the public transportation accessibility, so in the basic urban data for this planning support system, we have PDSTRE network and also the network for the public transportation, and also this planning process is converted into open source data
and open source data and analysis tools, and it is currently evaluated and evaluated by the urban planners.
So another issue is we have a design database, a standardized database, because we are applying this planning concept, but also the local city areas, so we have some criteria and
data for planning, and we have four categories for this planning, and the basic component of the data is some aggregation unit for analysis and network for network such as
PDSTRE and road network, and it is a case of a solar PDSTRE network and crosswalk, and we have a standardized and also standardizing the aggregation unit, and based on this unit
and aggregation unit and network, we are applying the walkability and also clustering the gender and age, so it is very different in terms of walkability according to age groups,
and also prioritizing the living area and get us some implication and currently designing the module using the Python libraries. Yeah, thank you.