The Urban Systems Lab (USL) Dataviz Platform is an interactive web application to visualize Social, Ecological, and Technological Systems (SETS). This platform is being used to encourage participatory processes, produce new knowledge, and facilitate collaborative analysis within nine Urban Resilience to Weather-related Extremes (UREx) Sustainability Research Network cities. It allows seamless shifts across contexts, scales, and perspectives for analysis within the SETS framework. How is digital space conceptualized for urban analysis and interventions? What is the capacity for reciprocal relationships between digital and physical space? How do we visually understand urban systems and complex spatial relationships? This chapter provides a comprehensive overview of the application stack and the different representational categories embedded in the Dataviz Platform. Offering a common visual language to various stakeholders, we explore new ground as we believe it has the potential to change how we think about, plan, and design our cities. (“Map devices such as a frame, scale, orientation, projection, indexing and naming reveal artificial geographies that remain unavailable to human eyes.” (Corner,.Cosgrove (ed), Mappings, Reaktion Books, London, 1999)
10.1 The USL Dataviz Platform
Facilitated by a UREx team of academic researchers, city planners, first responders, NGOs, and local community groups, the 3D visualizations featured on the USL Dataviz Platform (Sauter et al. 2019) are used to support cross-city analysis, collaboration, and conversation, with an individualized focus on local priorities in each city. For example, the Dataviz Platform has been used for stakeholder engagement in a number of UREx scenario workshops, providing the spatial context and SETS data to explore desirable and plausible pathways for reducing risks in improving resilience of vulnerable communities and critical infrastructure in cities such as Phoenix, Portland, Miami, and San Juan.
The platform is a public repository for a variety of visual outputs in one integrated project platform. It serves as an engagement tool and supports seamless shifts in context and perspective for analysis within a SETS framework (see Chap. 3); for instance, by switching from geospatial maps focused on heat (Fig. 10.1) to those focused on flood risk, and from city-wide analysis to block-level views. For the sake of inclusivity, common digital and mobile devices can be used to view the intricacies of geospatial layers presented on the platform in a simple and rich way. Additionally, the platform leverages common visual assets from location-based services and maps on mobile devices to relate to user expectations.
UREx city teams have used the platform to draft narratives in a content management system and share them publicly. For example, diverse stakeholders in Syracuse, New York have been able to use the platform to view important SETS data of the city together. This collaboration provides a basis for exploration of resilience futures, as well as a starting point for asking questions about the systemic interactions that drive current challenges in the city. The platform enables teams to manage the publication life cycle and add context and metadata to complex visual outputs such as future heat, flood, and land-use projections (e.g., UREx 2050 and 2080 scenarios). Doing so provides a better understanding of the underlying data and computational models that generated a particular output.
10.2 Representation of Space
In Social Justice and the City (1973), David Harvey argues the importance of reflecting on the nature of spaces to understand urban processes—what we call today “urban systems.” We use Harvey’s spatial classification from “Space as a Key Word” (2004) as a vocabulary to name the contextual and perspective shifts featured within the online USL Dataviz Platform:
Absolute, a fixed space, true to reality, one frame, independent
Relative, multiple geometries to choose from, “spatial frame depends upon what is being relativized and by whom” (Harvey 2004, p. 3)
Relational, processes occur and define their own spatial frame, object exists only in the relationship with another object
An interactive platform holds a dynamic range of these spaces (absolute, relative, and relational). Absolute space is represented by building footprints, land use, coastal flooding and everything that can be visualized and measured in space at scale (Fig. 10.2). Relative space is exponentially richer within an interactive tool, because the relativization changes according to user input, zoom levels, and data resolution. Because space is dynamic, data is relative to its container: pixels. While navigating the platform, data gets sorted, selected, abstracted, and slowly unpacked to add detail as the user moves closer in.
Data are meaningful on a screen insofar as it is visualized in its relation to other layers at different levels of abstraction. Sometimes the accuracy of elements in an (absolute) geospatial space is secondary; e.g., by using extruded building footprints as an approximation of a building’s shape. In other instances, visual elements represent data beyond its (relative) location; e.g., by showing the flow of people, a volume of water, or associated costs. In contrast, relational space is independent from absolute and relative dimensions, operating at the highest level of abstraction based on its own design rules; e.g., diagrams showing stakeholder networks, labor processes, or icons signifying sensations.
A good example for a dataset that shifts from a relative to absolute space paradigm is the green roofs layer in New York City (NYC); zooming out, we can see circles with some transparency showing the green roofs location in NYC (Fig. 10.3). These circles are not at scale but are able to show a distribution of green roofs across the city, relative to each other. As we zoom in, the circles disappear and transition into absolute space, uncovering both the geolocation and footprint of green roofs, as well as their elevation in three-dimensional space (Fig. 10.4).
Harvey further develops the classification where absolute, relative, and relational space intersects to create a matrix of combinations:
Material space, as in what you can experience with your senses
Representations of space, including diagrams, collages, infographics, writing, and geospatial maps. The visualization platform lies mostly in this category because it shows data within a specific location on earth, attributing information to the built environment (cities).
Spaces of representation, a symbolic space produced by cultural memories, symbols, emotions, images, and imaginaries such as artistic interpretations and narratives
Considering the different kinds of practices at work in the URExSRN, conceptualization of space varies considerably among the different stakeholders, and spans the full range of Harvey’s classification. “The problem of the proper conceptualization of space is resolved through human practice with respect to it.... The question what is space is therefore replaced by the question: How is it that different human practices create and make use of different conceptualizations of space?” (Harvey 1973, pp 13–14).
For example, a hydrologist might look at a particular storm event as a predictable nuisance that occurs at a certain frequency and can be represented accordingly. An emergency responder might view the same event through the emotional account of callers in distress. A resident (materially) experiences the event and its implications from a viewpoint centered at the actual site. And a City official manages those external forces and points of view to develop long-term resiliency plans, using spatial representations such as maps and diagrams to engage and communicate. The different stakeholders and practices therefore require a common (visual) language to bridge those gaps and points of view. This challenge is our objective, organizing a web of spatial relationships and representations into one integrated visualization platform.
10.3 Visualization Concepts
Web-based visualizations operate at the intersection of visual design, statistical analysis, and computer science (Drucker 2014). They create dynamic representations that can shift seamlessly between narrative modes and spatial representations. To balance the two for a range of use cases, the USL Dataviz Platform requires a high level of customization and is therefore unique in how it is constructed from source data to the finished design. Geospatial Information Systems (GIS) are historically used to produce maps for print media, and are tailored towards expert audiences versed in reading geospatial information. The following section discusses the different spatial capacities of the USL Dataviz Platform in relation to spatial categories that are inherent in working with GIS. They are arranged as opposite sides of a representational spectrum, along with a discussion of the design implications for the platform:
Static versus Dynamic: Static GIS maps often function as visual evidence to convey a particular point of view, placed intentionally into a larger narrative by an author. When visualizing data at different scales and levels of fidelity, two factors remain constant: the resolution of the underlying data and the size of the canvas it is displayed within. Hence, there are obvious limitations for constructing complex visualizations where time, space, and scale work interdependently together to produce a visual output within the constraints of the browser canvas. A framework that is flexible enough to shift spatial representations requires an interactive system that can process user inputs and render graphic outputs based on those interactions, dynamically. This approach has the advantageous capacity to do the following: Conduct self-serve analysis on various datasets, display dynamically updated (and real-time) data, facilitate collaboration and dissemination, provide varying levels of access, and collect structured feedback. Additionally, a dynamic system can harness principles of cinematography and motion graphics to tell stories in a linear or nonlinear fashion for different audiences and purposes, such as identifying risks, managing interventions, and planning adaptation strategies (Amini et al. 2015). Such participatory processes are fundamental for an inclusive approach, connecting researchers, practitioners, and residents around particular issues. To that end, a key feature of the platform is the ability to share a specific geospatial view through short URLs, available in the browser (bottom-right) corner, to show the exact same view and settings to a person who opens the link on another device.
Global versus Local: One of the primary affordances of interactive 3D maps is to provide viewers the freedom to seamlessly explore and compare the data at local, regional, national, and global scales. A resident engaged in a local issue, for instance, might therefore better understand how local social-ecological-technological systems, or SETS, compare to a regional picture. At the most local level (fully zoomed in), we use 3D extrusions of building footprints as a visual canvas to display the underlying data. The demographic data we use are accurate at a census block level, and while we extrude each building on the lot, the underlying data are not available at a lot level. Therefore, multiple buildings on the lot show the same census data. As a representation, building geometry (extruded from footprints using height information) provides a much better sense of the built environment, especially in urban settings, but also suggests higher accuracy—a trade-off and a design decision. Moving from a local to a regional zoom level, the accuracy of building geometry loses significance. Computationally, to display a city-wide view requires the ability to reduce complexity of the data to allow it to be rendered quickly in an online environment. This involves reducing the graphic complexity of buildings shown on the platform as the viewer moves around, thereby balancing the available computational resources and ensuring that the user experience is fluid. For example, showing all buildings on the more than one million building lots of NYC requires significant computation, and in order to find balance, thus requires reducing building detail to maintain a sufficient frame rate and a good user experience across a range of devices, including mobile phones. At a distance, administrative boundaries become more relevant and hold more weight for analysis, and the map geometry adapts accordingly into aggregate bins where necessary.
Raster versus Vector: Vector data format, which is essentially a database of point, line and polygon coordinates, is ideal for representing discrete data and geometries. It plays well with digital applications that demand a suite of interactions on top of the data layer, letting users “touch” and interact with the data itself. On the contrary, working with raster data is suitable for working with continuous data, where working with pixels instead of coordinates is a more computationally sensible approach. Such may be the case for working with digital elevation models (DEM), satellite imagery (Fig. 10.5) and similar remote sensing data. Depending on the workflow requirements, the data can be converted between raster to vector and vice versa (Fig. 10.6).
Narrative versus Representational: A key decision in the development of the USL Dataviz Platform was the objective to show qualitative data and narrative content developed during stakeholder workshops held in each network city (Fig. 10.7), and to convey connections between various vulnerabilities and exposures; for instance, food and energy security, coastal flooding, and urban and river flooding. Using an online editor, authors can draft narratives in the content management system, embed rich media such as images and movie clips, and link those with geospatial views and layers provided through the platform. Multiple views can be recorded and animated to tell a particular story, presented in a split-screen view that allows for analytic comparisons while navigating the narrative. This design choice balances the qualitative and quantitative representations that are displayed, while keeping the interactive features of the platform active. The design implication is the need for a highly customizable mapping engine,Footnote 1 along with the data structures that allow for customizable views, virtual camera settings, user permissions, and metadata. For data representations such as flood risk or land cover, one limitation of the popular Mapbox-GL engine is that it currently does not display elevation as 3D terrain, but as flat images with different color values corresponding to the height of the terrain.
10.4 Application Stack
The USL Dataviz Platform is implemented as a web application using open source libraries, and thus it can be replicated using standard web development tools and best practices.Footnote 2 The following workflow diagram broadly illustrates the end to end framework for creating spatial visualizations with the platform, presented as individual layers in the front-end web interface. These layers are interactively transformed client side by viewers through their individual web browsers (local storage) (Fig. 10.8).
The data visualization workflow operates as follows:
Data preparation: This stage involves collecting the geospatial data and applying necessary transformations, such as adjusting map projection, aggregation, filtering, geocoding, spatial joins, etc. to make the data interoperable with other server-side components for further processing. This is the most meticulous and time-consuming part of the process. The methodology varies highly for each dataset depending on the programmer’s domain expertise and desired output specifications for the visualization, as prioritized by local stakeholders.
Tile generation: Once the data are converted into the desired raster or vector format, the next step is to generate a tile map from it, generating a set of individual images that are loaded and displayed in the browser as a seamless map. Tippecanoe,Footnote 3 a command line utility, is a popular tool that we use to generate tiles from large datasets. It provides extensive options to customize both raster and vector data types.
Tile server: After encoding the map layers, tiles can be self-hosted on cloud infrastructure or commercial service providers.Footnote 4 Many libraries, like D3.jsFootnote 5or Deck.gl,Footnote 6 require data to be loaded within the client browser. However, streaming the complete datasets used to a client’s device is not feasible. When viewing large datasets in excess of a few megabytes, streaming the data as slippy mapsFootnote 7 is a standard and preferred approach. The data are split into a grid of tiles for each zoom level. Based on the web browser’s viewport, only the tiles within view are requested from the tile server and stitched together into a seamless canvas, which happens while zooming or panning a map. There are a number of tile server solutions available that can be deployed according to usage and demand.Footnote 8
Front-end application: The data are finally consumed and rendered by a front-end application operating on a user’s mobile or desktop device. It is responsible for accepting interactive user inputs and querying the data from the hosted tile server.
Our objectives are to support the exploration and understanding of complex geospatial relationships in a simple and organized way and to avoid visual outputs that cognitively overwhelm the viewer, both for a variety of use cases. By categorizing the representational concepts that inform our design decisions, the hidden operations within the visualization platform become more transparent. Intentionally, every design decision serves a specific narrative or representational purpose, which might not be apparent at first glance.
As explained by James Corner, maps are artificial geographies that reveal “the invisible” to the human eye. Whether in absolute, relative, or relational space, our interactive platform explores new ground in which maps function as dynamic data aggregates that shift scale and context, reveal transformative narratives, and enable participatory processes. This exploration has the potential to change how we think about, plan, and design our cities.
As a platform designed to provide spatial context and a digital common ground for various stakeholders in group settings, some viewers find the 3D environment more difficult to navigate than others, especially if they are not used to working with spatial data. Along with the spatial shifts from regional to local views, the “artificial geographies” produced by the platform can be cognitively taxing, especially when switching between different SETS data layers. Narrative text, hyperlinks, legends, and sources provide additional information through the platform’s narrative panel. This however also requires more in-depth study and sustained engagement in order to process and understand interconnected data.
The impact of the USL data visualization platform lies in its capacity to communicate the invisible through a common visual language, suitable for different human practices and stakeholders. As we continue to research and visualize urban social–ecological–technological systems in the future, we plan on pushing the boundaries between absolute and abstract spatial representations in contexts such as social relationships, traffic flows, green infrastructure, and questions of equity.
The USL Dataviz Team reviewed a set of mapping libraries to decide on the visualization software stack for the project, including MapBox-GL (which renders buildings as well as building-part extrusions), CesiumJS (using WebGL to render more detailed CityGML geometries), Mapbox SDK for Unity (useful for rendering extruded buildings over a terrain mesh, high-end graphics rendering, and post-processing), along with cloud-based mapping services such as Google Maps, Earth, and ArcGIS Online. Criteria for building a custom platform included the level of customizability, user experience across devices leveraging WebGL, cost to develop, scale, and host a tile server online, available support by a developer community, as well as the graphics roadmap for future development. The decision was to build the platform on Mapbox-GL.
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Editors and Affiliations
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Sauter, D., Randhawa, J., Tomateo, C., McPhearson, T. (2021). Visualizing Urban Social–Ecological–Technological Systems. In: Hamstead, Z.A., Iwaniec, D.M., McPhearson, T., Berbés-Blázquez, M., Cook, E.M., Muñoz-Erickson, T.A. (eds) Resilient Urban Futures. The Urban Book Series. Springer, Cham. https://doi.org/10.1007/978-3-030-63131-4_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63130-7
Online ISBN: 978-3-030-63131-4
Urban Systems Lab (USL) Dataviz Platform ni programu ya mtandao ya maingiliano ya kuona Mifumo ya Jamii, Ikolojia, na Teknolojia (SETS). Mfumo huu unatumika kuhamasisha michakato ya ushiriki, kuzalisha maarifa mapya, na kuwezesha uchambuzi wa ushirikiano ndani ya miji tisa ya Uwezo wa Mji wa Utafiti wa Uendelezaji wa Hali ya Hewa (URX). Inaruhusu mabadiliko ya moja kwa moja katika muktadha, viwango, na mitazamo kwa uchambuzi ndani ya mfumo wa SETS. Jinsi gani nafasi ya digital ni conceptualized kwa ajili ya uchambuzi wa mijini na uingiliaji? Ni nini uwezo wa mahusiano ya pande zote kati ya nafasi ya digital na kimwili? Tunatambuaje mifumo ya mijini na mahusiano magumu ya anga kwa njia ya kuona? Sura hii hutoa muhtasari wa kina wa stack ya maombi na makundi tofauti ya uwakilishi iliyoingizwa katika Jukwaa la Dataviz. Kwa kutoa lugha ya kawaida ya kuona kwa wadau mbalimbali, tunachunguza ardhi mpya kwa sababu tunaamini ina uwezo wa kubadilisha jinsi tunavyofikiria, kupanga, na kubuni miji yetu. "Kutumia ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani, ramani.""" "Mfano wa ""mfano wa msingi"" ni pamoja na uthibitisho wa ushirikiano wa kimataifa, ushirikiano wa kimataifa, na ushirikiano wa kimataifa, na lengo la kibinafsi juu ya vipaumbele vya ndani katika kila mji." Kwa mfano, DataViz Platform imetumika kwa ushiriki wa wadau katika warsha kadhaa za hali ya URX, ikitoa muktadha wa kijiografia na data ya SETS kuchunguza njia zinazofaa na za kuaminika za kupunguza hatari katika kuboresha uthabiti wa jamii dhaifu na miundombinu muhimu katika miji kama vile Phoenix, Portland, Miami na San Juan. Jukwaa ni hazina ya umma kwa ajili ya aina mbalimbali ya matokeo ya kuona katika moja jumuishi mradi jukwaa. Ni kutumika kama chombo ushiriki na inasaidia mabadiliko seamless katika muktadha na mtazamo kwa ajili ya uchambuzi ndani ya mfumo SETS (tazama sura ya 4). 3); kwa mfano, kwa kubadili kutoka ramani geospatial ililenga joto (Fig. "Kama ilivyoelezwa hapo juu, ""mfano wa mafuriko"" ni ""mfano wa mafuriko"" na ""mfano wa mafuriko"" ni ""mfano wa mafuriko"" na ""mfano wa mafuriko"" ni ""mfano wa mafuriko"" na ""mfano wa mafuriko"" ni ""mfano wa mafuriko.""" Kwa ajili ya ujumuishaji, vifaa vya kawaida vya dijiti na vya rununu vinaweza kutumika kuona utata wa tabaka za kijiografia zilizowasilishwa kwenye jukwaa kwa njia rahisi na tajiri. Kwa kuongezea, jukwaa linatumia mali ya kawaida ya kuona kutoka kwa huduma za msingi wa eneo na ramani kwenye vifaa vya rununu kuhusiana na matarajio ya mtumiaji. Timu za jiji la UREx zimetumia jukwaa hilo kuunda hadithi katika mfumo wa usimamizi wa maudhui na kuzishiriki hadharani. Kwa mfano, wadau mbalimbali katika Syracuse, New York wamekuwa na uwezo wa kutumia jukwaa la kuona muhimu SETS data ya mji pamoja. Ushirikiano huu hutoa msingi wa kuchunguza siku zijazo za upinzani, na pia hatua ya kuanzia kwa kuuliza maswali juu ya mwingiliano wa mfumo ambao huendesha changamoto za sasa katika jiji. Programu hii inasaidia timu kusimamia mzunguko wa maisha ya kuchapisha na kuongeza muktadha na metadata kwa matokeo ya kuona tata kama vile joto la baadaye, mafuriko, na utabiri wa matumizi ya ardhi (kwa mfano, hali ya hali ya hewa ya 2050 na 2080). Kufanya hivyo hutoa uelewa bora wa data ya msingi na mifano ya mahesabu ambayo ilizalisha pato fulani. "Kama ilivyoelezwa katika ""Representation of Space in Social Justice and the City"" (mwaka wa 2003), Harvey anatumia ""space classification"" kutoka kwa ""Space as a Keyword"" kama msamiati wa kutaja mabadiliko ya muktadha na mtazamo yaliyoonyeshwa ndani ya jukwaa la data la USL: Absolute, nafasi iliyowekwa, kweli kwa hali halisi, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja, moja." "Uwanja kamili unawakilishwa na ""kujenga nyayo za miguu,"" ""matumizi ya ardhi,"" ""mafuriko ya pwani"" na ""kila kitu ambacho kinaweza kuonekana na kupimwa katika nafasi kwa kiwango.""" 10.2. Nafasi ya jamaa ni exponentially tajiri ndani ya chombo maingiliano, kwa sababu relativization mabadiliko kulingana na pembejeo ya mtumiaji, viwango vya zoom, na azimio data. Kwa sababu nafasi ni nguvu, data ni jamaa na chombo chake: pixels. Wakati wa kusafiri kwenye jukwaa, data hupangwa, kuchaguliwa, kuchukuliwa, na kupunguzwa polepole ili kuongeza maelezo wakati mtumiaji anakaribia. Data ni muhimu kwenye skrini kwa kadiri inavyoonekana katika uhusiano wake na tabaka zingine katika viwango tofauti vya abstraction. Wakati mwingine usahihi wa vipengele katika (hakika) geospatial nafasi ni sekondari; kwa mfano, kwa kutumia extruded jengo nyayo kama approximation ya jengo's sura. Katika visa vingine, vipengele vya kuona huwakilisha data zaidi ya eneo lake (la jamaa); kwa mfano, kwa kuonyesha mtiririko wa watu, kiasi cha maji, au gharama zinazohusiana. Kwa kulinganisha, nafasi ya uhusiano ni huru kutoka kwa vipimo vya jumla na vya jamaa, ikifanya kazi katika kiwango cha juu cha abstraction kulingana na sheria zake za kubuni, kwa mfano, michoro inayoonyesha mitandao ya wadau, michakato ya kazi, au ikoni zinazowakilisha hisia. Mfano mzuri wa seti ya data ambayo inabadilika kutoka kwa paradigm ya nafasi ya jamaa hadi kamili ni safu ya paa za kijani huko New York City (NYC); kukua nje, tunaweza kuona duru na uwazi fulani unaoonyesha eneo la paa za kijani huko New York. 10.3) Mzunguko huu haupo kwa kiwango lakini una uwezo wa kuonyesha usambazaji wa paa za kijani kote jijini, kuhusiana na kila mmoja. Kama sisi zoom katika, duru kutoweka na mabadiliko katika nafasi kamili, kufunua wote geolocation na alama ya paa kijani, kama vile urefu wao katika nafasi tatu-dimensional. 10.4. "Harvey pia anaelezea jinsi nafasi ya ""mwisho,"" ""mahusiano,"" na ""mahali pa ukaribu"" vinavyounganishwa na ""mahali pa nyenzo,"" ""mahali pa vifaa,"" ""mahali pa vitu"" na ""mahali pa picha,"" ""mahali pa picha,"" ""mahali pa picha,"" ""mahali pa picha,"" ""mahali pa picha.""" Visual jukwaa liko zaidi katika jamii hii kwa sababu inaonyesha data ndani ya eneo maalum juu ya dunia, attributing habari kwa mazingira kujengwa (miji). Maonyesho ya nafasi, nafasi ya ishara inayotokana na kumbukumbu za kitamaduni, alama, hisia, picha, na mawazo kama vile tafsiri za kisanii na hadithi, ikizingatia aina tofauti za mazoea katika kazi ya URXSR, dhana ya nafasi inatofautiana sana kati ya wadau tofauti, na inashughulikia aina kamili ya uainishaji wa Harvey. <unk> Tatizo la dhana sahihi ya nafasi ni kutatuliwa kwa njia ya mazoezi ya binadamu kuhusiana na hilo.... "Kwa hivyo swali ""ni nini nafasi"" linabadilishwa na swali ""Ni vipi kwamba mazoea tofauti ya kibinadamu huunda na kutumia dhana tofauti za nafasi?"" (Harvey, 1973, p.13-14)." Kwa mfano, mtaalamu wa maji anaweza kuangalia tukio fulani la dhoruba kama usumbufu unaoweza kutabiriwa ambao hutokea kwa mzunguko fulani na unaweza kuwakilishwa ipasavyo. Msaidizi wa dharura aweza kuona tukio hilohilo kupitia simulizi la kihisia-moyo la wapiga-simu walio katika taabu. Mkaaji (kinyume) uzoefu tukio na athari zake kutoka mtazamo katikati ya tovuti halisi. Maafisa wa jiji husimamia nguvu hizo za nje na maoni ili kuendeleza mipango ya muda mrefu ya upinzani, kwa kutumia uwakilishi wa nafasi kama ramani na michoro ya kujihusisha na kuwasiliana. Washiriki tofauti na mazoea kwa hiyo zinahitaji lugha ya kawaida (ya kuona) ili kuziba mapungufu hayo na maoni. Hii ni changamoto yetu lengo, kuandaa mtandao wa mahusiano ya anga na uwakilishi katika moja jumuishi visualization jukwaa. "Kufanya kazi kwa njia ya ""kuona"" ni njia ya kuonekana kwa njia ya mtandao, ambayo ni njia ya kuonekana kwa njia ya picha, na kwa njia ya uchambuzi wa takwimu na sayansi ya kompyuta (Drucker, 2014)." Wao kuunda uwakilishi nguvu ambayo inaweza kubadilisha seamlessly kati ya modes hadithi na uwakilishi nafasi. Ili kusawazisha mbili kwa aina ya kesi za matumizi, USL DataViz Platform inahitaji kiwango cha juu cha ubadilishaji na kwa hivyo ni ya kipekee katika jinsi inavyojengwa kutoka kwa data ya chanzo hadi muundo wa kumaliza. Geospatial Information Systems (GIS) ni mapato ya zamani ya kuchapishwa kwa vyombo vya habari, na ni maalum kwa ajili ya wasikilizaji wa wataalamu wanaojua kusoma habari za kijiografia. Sehemu inayofuata inazungumzia uwezo tofauti wa nafasi ya USL DataViz Platform kuhusiana na makundi ya nafasi ambayo ni asili ya kufanya kazi na GIS. Picha za GIS zimepangwa kama pande tofauti za wigo wa uwakilishi, pamoja na majadiliano ya athari za kubuni kwa jukwaa: Static dhidi ya Dynamic: Ramani za GIS za Static mara nyingi hufanya kazi kama ushahidi wa kuona ili kuwasiliana na mtazamo maalum, uliowekwa kwa makusudi katika hadithi kubwa na mwandishi. Wakati wa kuonekana kwa data katika viwango tofauti na viwango vya uaminifu, mambo mawili hubaki mara kwa mara: azimio la data ya msingi na ukubwa wa canvas inayoonyeshwa ndani yake. Kwa hiyo, kuna mapungufu ya wazi kwa ajili ya kujenga visualizations tata ambapo muda, nafasi, na kiwango kazi interdependently pamoja kuzalisha pato Visual ndani ya vikwazo vya browser canvas. Mfumo ambao ni rahisi kutosha kuhamisha uwakilishi wa anga inahitaji mfumo wa maingiliano ambayo inaweza kusindika pembejeo ya mtumiaji na kutoa matokeo ya picha kulingana na maingiliano hayo, kwa nguvu. Njia hii ina uwezo wa kufanya yafuatayo: Kufanya self-service uchambuzi juu ya seti mbalimbali za data, kuonyesha dynamically updated (na wakati halisi) data, kuwezesha ushirikiano na usambazaji, kutoa ngazi mbalimbali za upatikanaji, na kukusanya maoni muundo. Kwa kuongezea, mfumo wa nguvu unaweza kutumia kanuni za sinema na picha za mwendo kuelezea hadithi kwa mtindo wa linear au nonlinear kwa watazamaji tofauti na madhumuni, kama vile kutambua hatari, kusimamia uingiliaji, na kupanga mikakati ya marekebisho. 2015) na Utaratibu kama huo wa ushiriki ni wa msingi kwa mbinu ya kujumuisha, kuunganisha watafiti, watendaji, na wakazi karibu na masuala maalum. Kwa kusudi hilo, kipengele muhimu cha jukwaa ni uwezo wa kushiriki mtazamo maalum wa kijiografia kupitia URL fupi, zinazopatikana katika kona ya chini ya kulia ya kivinjari, kuonyesha mtazamo sawa na mipangilio kwa mtu anayefungua kiungo kwenye kifaa kingine. Utafiti wa 3D wa kimataifa na wa ndani: Moja ya faida kuu za ramani za maingiliano za 3D ni kuwapa watazamaji uhuru wa kuchunguza na kulinganisha data kwa urahisi katika viwango vya ndani, kikanda, kitaifa, na kimataifa. Kwa mfano, mtu anayefanya kazi katika eneo fulani anaweza kuelewa vizuri jinsi mifumo ya kijamii, ikolojia na kiteknolojia inavyoweza kulinganishwa na eneo hilo. Katika kiwango cha ndani kabisa (kuongeza kabisa), tunatumia extrusions 3D ya alama za ujenzi kama canvas ya kuona kuonyesha data ya msingi. Takwimu za idadi ya watu tunazotumia ni sahihi katika kiwango cha block ya sensa, na wakati tunatoa kila jengo kwenye lot, data ya msingi haipatikani katika kiwango cha lot. Kwa hiyo, majengo mengi kwenye eneo hilo yanaonyesha data ileile ya sensa. Kama uwakilishi, ujenzi jiometri (extruded kutoka nyayo kutumia urefu habari) hutoa hisia bora sana ya mazingira kujengwa, hasa katika mazingira ya mijini, lakini pia inaonyesha usahihi wa juu <unk> biashara-off na uamuzi wa kubuni. Kuhama kutoka kiwango cha ndani hadi kiwango cha kikanda cha zoom, usahihi wa jiometri ya jengo hupoteza umuhimu. Kwa computationally, kuonyesha mtazamo wa mji wote inahitaji uwezo wa kupunguza utata wa data kuruhusu kuwa na haraka katika mazingira ya mtandaoni. Hii inahusisha kupunguza ugumu wa picha za majengo yaliyoonyeshwa kwenye jukwaa wakati mtazamaji anapoenda, na hivyo kusawazisha rasilimali za hesabu zinazopatikana na kuhakikisha kuwa uzoefu wa mtumiaji ni laini. Kwa mfano, kuonyesha majengo yote kwenye zaidi ya milioni moja ya majengo mengi ya NYC inahitaji hesabu kubwa, na ili kupata usawa, hivyo inahitaji kupunguza maelezo ya jengo ili kudumisha kiwango cha sura ya kutosha na uzoefu mzuri wa mtumiaji katika aina ya vifaa, ikiwa ni pamoja na simu za mkononi. Kwa mbali, mipaka ya kiutawala inakuwa muhimu zaidi na kushikilia uzito zaidi kwa ajili ya uchambuzi, na jiometri ya ramani inabadilika ipasavyo katika mabati ya jumla inapohitajika. Vector: Vector data format, ambayo ni msingi database ya pointi, mstari na poligoni kuratibu, ni bora kwa ajili ya kuwakilisha data tofauti na jiometri. Ni kucheza vizuri na maombi ya digital ambayo kudai suite ya maingiliano juu ya safu ya data, kuruhusu watumiaji <unk>touch<unk> na kuingiliana na data yenyewe. Kwa upande mwingine, kufanya kazi na raster data ni yanafaa kwa ajili ya kufanya kazi na data kuendelea, ambapo kufanya kazi na pixels badala ya kuratibu ni zaidi computationally busara mbinu. Hii ni kwa sababu ya kuunganisha na mifano ya digital ya urefu (DEM) na picha za satellite. (a) (b) (c) (d) (d) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) (e) ( Kulingana na mahitaji ya mtiririko wa kazi, data inaweza kubadilishwa kati ya raster kwa vector na kinyume chake (Fig. 10.6 (Kichwa cha habari) Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi wa Ufafanuzi. "Kama ilivyoelezwa katika sehemu ya 10, ""kuelezea uhusiano kati ya udhaifu mbalimbali na mfiduo, kwa mfano, usalama wa chakula na nishati, mafuriko ya pwani, na mafuriko ya mijini na mito.""" Kwa kutumia mhariri wa mtandaoni, waandishi wanaweza kuchora hadithi katika mfumo wa usimamizi wa maudhui, kuingiza vyombo vya habari tajiri kama vile picha na vipindi vya filamu, na kuunganisha wale na maoni ya kijiografia na tabaka zinazotolewa kupitia jukwaa. Maoni mengi yanaweza kurekodiwa na kuonyeshwa ili kuelezea hadithi fulani, iliyowasilishwa katika mtazamo wa skrini iliyogawanyika ambayo inaruhusu kulinganisha uchambuzi wakati wa kusafiri hadithi. Uchaguzi huu wa kubuni unalinganisha uwakilishi wa ubora na kiasi unaonyeshwa, wakati wa kuweka vipengele vya maingiliano vya jukwaa hai. "Kama matokeo ya kubuni, kuna haja ya injini ya ramani ya ""kufanya kazi"" yenye kubadilika sana, pamoja na miundo ya data ambayo inaruhusu maoni ya kubadilika, mipangilio ya kamera ya virtual, ruhusa za mtumiaji, na metadata." Kwa uwakilishi wa data kama hatari ya mafuriko au kifuniko cha ardhi, kikomo kimoja cha injini maarufu ya MapBox-GL ni kwamba kwa sasa haionyeshi urefu kama eneo la 3D, lakini kama picha za gorofa na maadili tofauti ya rangi yanayolingana na urefu wa eneo. "Utaratibu wa ""Utaratibu wa Utaratibu"" ni mfumo wa programu ya wavuti unaotumia vifaa vya wazi vya chanzo, na kwa hivyo unaweza kuiga kwa kutumia zana za kawaida za maendeleo ya wavuti na mazoea bora. ""Utaratibu wa programu"" ni mfumo wa programu ya wavuti unaotumia vifaa vya wazi vya chanzo." Tabaka hizi ni interactively kubadilishwa upande mteja na watazamaji kupitia browsers yao binafsi mtandao (kuhifadhi ndani) (Fig. 10.8 (Kichwa cha habari) Kazi ya data visualization kazi kama ifuatavyo: Data maandalizi: Hatua hii inajumuisha kukusanya data geospatial na kutumia mabadiliko muhimu, kama vile kurekebisha ramani makadirio, mkusanyiko, kuchuja, geocoding, spatial kuungana, na kadhalika. Data inaweza kuwa na uwezo wa kufanya kazi na vipengele vingine vya server-upande kwa ajili ya usindikaji zaidi. Hii ndiyo sehemu yenye uangalifu zaidi na inayotumia wakati mwingi zaidi katika mchakato huo. Mbinu inatofautiana sana kwa kila dataset kulingana na mtaalam wa uwanja wa programu na vipimo vya pato vinavyotakiwa kwa ajili ya kuona, kama ilivyowekwa na wadau wa ndani. Baada ya data kugeuzwa katika raster au vector format, hatua ya pili ni kuzalisha ramani tile kutoka kwake, kuzalisha seti ya picha za mtu binafsi ambayo ni kupakiwa na kuonyeshwa katika kivinjari kama ramani seamless. Tippecanoe ni kifaa cha mstari wa amri ambacho hutumiwa kutengeneza data kutoka kwa seti kubwa za data. Ni hutoa chaguzi pana kwa ajili ya kubadili wote raster na vector aina ya data. Baada ya kuunda tabaka za ramani, tile zinaweza kuhifadhiwa kwenye miundombinu ya wingu au watoa huduma za kibiashara.Kifurushi cha D3.js, D3.js, au Dk.gl inahitaji data kupakiwa ndani ya kivinjari cha mteja. Hata hivyo, utiririshaji wa seti kamili ya data kutumika kwa kifaa mteja si iwezekanavyo. Wakati wa kutazama seti kubwa za data zaidi ya megabytes chache, kusambaza data kama ramani za slippy ni mbinu ya kawaida na inayopendekezwa. Takwimu ni kugawanywa katika gridi ya tiles kwa kila ngazi ya zoom. Kulingana na kivuli cha kivinjari cha wavuti, tu vigae ndani ya mtazamo huombwa kutoka kwa seva ya vigae na kushona pamoja katika canvas isiyo na mshono, ambayo hutokea wakati wa kuongeza au kuhamisha ramani. Kuna idadi ya tile server ufumbuzi inapatikana ambayo inaweza kutumika kulingana na matumizi na mahitaji.Footnote 8 Front-end maombi: Data ni hatimaye kutumika na rendered na front-end maombi kuendesha juu ya mtumiaji ya simu au desktop kifaa. Ni wajibu wa kukubali maingilio ya mtumiaji maingiliano na kuuliza data kutoka server tile mwenyeji. Lengo ni kusaidia uchunguzi na uelewa wa mahusiano tata geospatial kwa njia rahisi na kupangwa na kuepuka matokeo ya kuona kwamba cognitively kuzidisha mtazamaji, wote kwa ajili ya aina ya kesi ya matumizi. Kwa kuainisha dhana za uwakilishi ambazo zinaarifu maamuzi yetu ya kubuni, shughuli zilizofichwa ndani ya jukwaa la kuona zinakuwa wazi zaidi. Kwa makusudi, kila uamuzi wa kubuni hutumikia kusudi maalum la hadithi au uwakilishi, ambalo linaweza kuwa halionekani kwa mtazamo wa kwanza. Kama ilivyoelezwa na James Corner, ramani ni jiografia bandia ambayo hufunua "kile kisichoonekana" kwa jicho la binadamu. Iwe katika nafasi ya jumla, ya jamaa, au ya uhusiano, jukwaa letu la maingiliano linachunguza ardhi mpya ambayo ramani hufanya kazi kama jumla ya data ya nguvu ambayo hubadilisha kiwango na muktadha, inafunua hadithi za mabadiliko, na kuwezesha michakato ya ushiriki. Uchunguzi huu una uwezo wa kubadilisha jinsi tunavyofikiria, kupanga, na kubuni miji yetu. Kama jukwaa iliyoundwa kutoa mazingira ya kijiografia na msingi wa dijiti kwa wadau mbalimbali katika mazingira ya kikundi, baadhi ya watazamaji hupata mazingira ya 3D ngumu zaidi kuendesha kuliko wengine, hasa kama hawajazoea kufanya kazi na data ya kijiografia. Pamoja na mabadiliko ya nafasi kutoka kwa maoni ya kikanda hadi ya ndani, "jografia bandia" zinazozalishwa na jukwaa zinaweza kuwa na ushuru wa utambuzi, haswa wakati wa kubadili kati ya tabaka tofauti za data za SETS. Nakala ya hadithi, viungo, hadithi, na vyanzo hutoa habari ya ziada kupitia jopo la hadithi la jukwaa. Hata hivyo, hii pia inahitaji utafiti wa kina zaidi na ushiriki endelevu ili kusindika na kuelewa data zilizounganishwa. Uwezo wa jukwaa la kuona data la USL unategemea uwezo wake wa kuwasiliana na usioonekana kupitia lugha ya kawaida ya kuona, inayofaa kwa mazoea tofauti ya kibinadamu na wadau. Kama sisi kuendelea utafiti na visualize mijini kijamii <unk> ikolojia <unk> mifumo ya kiteknolojia katika siku zijazo, sisi mpango wa kushinikiza mipaka kati ya kamili na abstract maonyesho ya anga katika muktadha kama vile mahusiano ya kijamii, mtiririko wa trafiki, miundombinu ya kijani, na maswali ya usawa. Timu ya USL DataViz ilichunguza seti ya maktaba za ramani ili kuamua juu ya stack ya programu ya kuona kwa mradi, ikiwa ni pamoja na MapBox-GL (ambayo hutoa majengo na extrusions ya sehemu ya jengo), CesiumJS (kutumia WebGL kutoa jiometri za CityGML za kina zaidi), MapBox SDK kwa Umoja (faida kwa kutoa majengo ya extruded juu ya mesh, graphics ya hali ya juu, na baada ya usindikaji), pamoja na huduma za ramani za wingu kama Google Maps, Earth, na ArcGIS Online. Vipimo vya kujenga jukwaa la desturi ni pamoja na kiwango cha customizability, uzoefu wa mtumiaji katika vifaa vya kutumia WebGL, gharama ya kuendeleza, kiwango, na mwenyeji wa seva ya tile mtandaoni, msaada unaopatikana na jamii ya watengenezaji, na pia ramani ya ramani ya maendeleo ya baadaye. Uamuzi ulikuwa kujenga jukwaa juu ya Mapbox-GL. Amini F., Henry R. N. et al., Lee H. et al., 2015 - Kuelewa video za data: kuangalia taswira ya hadithi kupitia lensi ya kinematografia. "Mkutano wa kila mwaka wa ACM wa 33 juu ya Mambo ya Binadamu katika Mifumo ya Kompyuta, Seoul, Korea Kusini, ukurasa 1459-1468, Corner J (1999) ""Agency of Mapping: Speculation, Criticism and Invention.""" Katika: Cosgrove D (ed) Mappings. Reaction Books, London, pp. 231, 252, Drucker J. (2014) Graphesis: aina za kuona za uzalishaji wa maarifa (metaLAB miradi). Harvard University Press, Cambridge, MA Harvey D (1973) Haki ya kijamii na mji. John Hopkins University Press, Baltimore, D. Harvey (2004) Nafasi kama neno muhimu. "Mkutano wa Marx na Falsafa, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London, London.""" . Biosci 66 (3): 198 <unk> 212. "Mfano: ""Sautiers"" (Randhawa J. McPherson, T. 2019) na ""USL DataViz Platform"" (USL DataViz) ni mifumo ya data ya data ya data." "Ndio maana, ""Sistema de Slabs"" (Sistema de Slabs) ni mfumo wa ujenzi wa barabara." "Kutoka kwa ""Makampuni ya Uuzaji na Uuzaji"" ni kazi ya serikali ya Marekani na haiko chini ya ulinzi wa haki za uchapaji nchini Marekani; ulinzi wa haki za uchapaji wa kigeni unaweza kutumika kuhusu sura hii." Kuonyesha Miji Jamii <unk> Ekolojia <unk> Teknolojia Systems. Katika: Hamstead, Z.A., Iwaniec, D.M., McPhearson, T., Berbés-Blázquez, M., Cook, E.M., Muñoz-Erickson, T.A. (eds) Resilient Urban Futures. Mfululizo wa Vitabu vya Mjini. Springer, Cham. "Kama ilivyoelezwa katika makala ya kwanza ya ""Springer"" ya ""Springer"" na ""Springer"" ya ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Springer"" na ""Sprinter"" na ""Sprinter"""