Relevant bibliographies by topics / Urban Mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Urban Mining'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 March 2023

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Journal articles on the topic "Urban Mining"

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Zhu, Xuan. "GIS and Urban Mining." Resources 3, no. 1 (March 3, 2014): 235–47. http://dx.doi.org/10.3390/resources3010235.

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Boeckh, Martin. "Urban Mining der besonderen Art." ENTSORGA-Magazin 40, no. 2 (2021): 73. http://dx.doi.org/10.51202/0933-3754-2021-2-073-2.

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Armisheva, Galiya, Natalia Sliusar, and Vladimir Korotaev. "Briefing: Urban-mining of landfills." Proceedings of the Institution of Civil Engineers - Waste and Resource Management 166, no. 4 (November 2013): 153–57. http://dx.doi.org/10.1680/warm.12.00025.

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Franke, Matthias, Mario Mocker, and Ingrid Löh. "Urban Mining – Wertstoffgewinnung aus Abfalldeponien." Wasser und Abfall 13, no. 3 (March 2011): 40–46. http://dx.doi.org/10.1365/s35152-011-0023-2.

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Cossu, Raffaello, and Ian D. Williams. "Urban mining: Concepts, terminology, challenges." Waste Management 45 (November 2015): 1–3. http://dx.doi.org/10.1016/j.wasman.2015.09.040.

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Katakis, Ioannis. "Mining urban data (part A)." Information Systems 54 (December 2015): 113–14. http://dx.doi.org/10.1016/j.is.2015.08.002.

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Andrienko, Gennady, Dimitrios Gunopulos, Yannis Ioannidis, Vana Kalogeraki, Ioannis Katakis, Katharina Morik, and Olivier Verscheure. "Mining Urban Data (Part B)." Information Systems 57 (April 2016): 75–76. http://dx.doi.org/10.1016/j.is.2016.01.001.

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Andrienko, Gennady, Dimitrios Gunopulos, Yannis Ioannidis, Vana Kalogeraki, Ioannis Katakis, Katharina Morik, and Olivier Verscheure. "Mining Urban Data (Part C)." Information Systems 64 (March 2017): 219–20. http://dx.doi.org/10.1016/j.is.2016.09.003.

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Billard, Isabelle. "Green solvents in urban mining." Current Opinion in Green and Sustainable Chemistry 18 (August 2019): 37–41. http://dx.doi.org/10.1016/j.cogsc.2018.11.013.

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Qian, Jingyi. "Retrofitting Existing Urban Voids." BCP Education & Psychology 7 (November 7, 2022): 327–34. http://dx.doi.org/10.54691/bcpep.v7i.2684.

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As an abandoned mining area, Huanglong Mountain Purple Sand Mining Area in Dingshu Town, Yixing City, Jiangsu Province has entered a state of suspension of production. This study draws on successful cases and retrofits the abandoned mining area in Huanglong Mountain into a “source of purple sand” community park. With purple sand (the represent of the urban) as the core, museums are built to realize the restoration of the ruins and at the same time display the culture as well as history of the development of purple sand mining and pottery industry through photos and historical documents. This community park also provides ordinary pottery artists with opportunities to display their works and participate in cultural inheritance through workshop, narrating their learning experience to encourage youngsters. Some basic facilities like teahouses, restaurants, hotels and bazaars associated with local elements are created in the park to help tourists to experience the local pottery art life and boost economy prosperity of the city.
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Dissertations / Theses on the topic "Urban Mining"

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Lilliemarck, Jakob. "Super Local Urban Mining." Thesis, Konstfack, Industridesign, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:konstfack:diva-4185.

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Anesie, Laura Noemi. "Urban Mining in Malmö - An Investigative Study to Identify the Potential of Urban Mining." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-23943.

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This master thesis depicts the topic of urban mining and its possibilities and challenges in the city of Malmö. Because of present day’s high consumption and construction our resources are becoming scarcer. In order to continue to build and consume the way we do, we need to look at other alternatives to obtain these resources. One sustainable alternative is urban mining which is based on society as a resource base where material accumulating over time is a metal storage that can be used through reuse or recycling. This thesis is limited to one type of urban mining which refers to unused cables and pipes that lie underground, so called hibernating cables. The purpose of this thesis is to explore the possibilities and challenges with urban mining in the city of Malmö and to research the Kabel-x urban mining method and its implementation possibilities. To successfully understand the challenges and possibilities a qualitative approach was taken where semi-structured interviews were conducted to see attitudes towards urban mining as well as to identify stakeholders who would work with an urban mining project in the future. The qualitative approach was complemented by a literary research which built the theoretical framework with theories like urban mining, urban metabolism and material flow analysis and sustainability assessments. The empirical discoveries depict topics such as ownership, knowledge-gap or skepticism when it comes to urban mining as well as methods of extractions, but also point to high interest and economic incentives with are in concordance with sustainability aspects. This lead to the conclusion that urban mining shows both possibilities and challenges in Malmö, which proves a challenging but worth implementation. Regarding Kabel-x method, its sustainability aspects and challenges with its implementation, it was concluded that on account of mostly skepticism and knowledge-gap stakeholders proved its implementation challenging but also interesting for urban development.
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AMATO, ALESSIA. "Innovative and sustainable strategies of urban mining." Doctoral thesis, Università Politecnica delle Marche, 2017. http://hdl.handle.net/11566/245303.

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La gestione di un’enorme quantità di rifiuti da apparecchiature elettriche ed elettroniche (RAEE), rappresenta un problema rilevante per la nostra società, poichè rischi per l’ambiente e la salute umana, legati ad una scorretta gestione, sono combinati con la perdita di materiali valorizzabili. Questo lavoro ha per oggetto lo sviluppo di processi sostenibili per il recupero di metalli di valore dai RAEE: in particolare, è stata effettuata un’indagine in laboratorio mirata all’estrazione, da schermi a cristalli liquidi, di indio, un metallo recentemente classificato dalla Commissione Europea tra i “critical raw materials”. La sperimentazione ha permesso l’ottimizzazione di un processo con rese di recupero di indio superiori al 90%, basato su operazioni idrometallurgiche. E’ stato studiato inoltre il processo dal punto di vista della sua sostenibilità ambientale, confrontandone l’impatto con quello degli attuali sistemi di gestione degli schermi a cristalli liquidi . La valutazione ha evidenziato che il ciclo di gestione delle acque di processo e pre-trattamenti fisici del pannello finalizzati alla concentrazione del metallo, rappresentano dei fattori chiave per la sostenibilità ambientale del processo. Il lavoro è stato svolto nel contesto di un progetto finanziato dalla Commissione Europea nell’ambito del 7FP, denominato HydroWEEE. Tale progetto aveva per obiettivo la realizzazione di un impianto mobile, con caratteristiche flessibili per il recupero di metalli da diversi RAEE: indio da TV/monitor a cristalli liquidi, ittrio da lampade e tubi catodici, rame oro e argento da circuiti stampati, cobalto da batterie litio-ione. L’attività di ricerca è stata anche finalizzata a valutare la sostenibilità ambientale dei vari processi realizzati nell’impianto mobile, evidenziandone un generale vantaggio (tra il 20 e l’80%) rispetto alla produzione primaria dei metalli. La valutazione dei rischi per i lavoratori nell’impianto mobile conclude lo studio.
The management of a huge quantity of waste from electric and electronic equipment (WEEE) represents a critical issue for the modern society. The negative environmental and health effects due to the improperly management are combined with the loss of valuable materials. The present work focused on the recovery of metals from WEEE with particular attention to indium from end-of-life liquid crystal displays (LCD). The experimental section allowed the optimization of a process that includes an acid leaching characterized by an innovative cross-current design, followed by a cementation with zinc powder. Considering the satisfying efficiencies obtained on the lab scale, higher than 90%, the whole process was studied from an environmental point of view comparing its emissions with those produced by the current management strategies (disposal in landfilling sites, incineration and traditional recycling). A life cycle assessment (LCA) of the different scenarios proved the significant advantage of recycling ways. Moreover, the traditional recycling resulted to be the most favorable, due for both the relevant water consumption of the innovative treatment and to the low indium content in the LCD. Nevertheless, a simple water recirculation system, combined with a physical indium upgrading in the waste, make the innovative option the best choice. The simple design of the optimized process allows its implementation in a mobile plant, built within the European project, HydroWEEE. The plant mobility prevents the impacts due to the waste transport, that contributes to the 30-40% of the currently treatments. Furthermore, this advantage is combined with the possibility to treat several WEEE for the recovery of different metals. The sustainability of this approach was proved by a LCA that highlighted the positive effect also in the comparison with the primary production, with a benefit between 20 and 80%. Last, but not least, the risk for workers in the real mobile plant was assessed.
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Iattoni, Giulia. "Electronic waste: hazards and opportunities for urban mining." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17822/.

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Since a couple of decades, society has been revolutionized by electrical and electronic equipment: it is ubiquitous and once discarded it contributes to generate one of the fastest-growing waste stream categories at this time. The purpose of this study is to discuss the complex nature of the WEEE sector intended as indispensable resource, including also the specific hazards that must be considered for a proper and valorising management. The first chapter introduces the wide background of urban waste in terms of production, collection and disposal. Then, an overview on the flows, classification and legal framework of electronic waste is provided. In the second chapter the analysis will focus on the improper management of e-waste which is extremely intensive and risky, therefore several possible situations will be qualitatively investigated in terms of environmental impacts and risks for human health. The third chapter will explore the concept of urban mining secondary raw materials in the context of e-waste, pointing out the current state of innovation, future challenges and present limitations. The fourth and last chapter of the study applies the Life Cycle Assessment methodology on waste mobile phones for three different End-of-Life scenarios. The aim is to demonstrate through a scientifically based tool the concepts presented in the previous part of the thesis and to outline the environmental benefits of components and materials recovery in terms of saved emissions through the evaluation of three impact categories: Global Warming Potential, Acidification Potential and Ecological Scarcity. Given from one hand the huge quantities involved in the WEEE sector in terms of volumes and impacts, and from the other the scarcity and increasing demand of raw materials, it is reasonable to consider e-waste as a key element to work on in order to adress some of the Sustainable Development Goals adopted by the United Nations.
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Ekholm, Disa, Alice Hallberg, Ellen Stenlund, Johan Wallsten, and Sara Westerström. "Urban mining - Återvinning av byggnadsmaterial i främre Boländerna." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-411757.

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Boländerna är ett äldre industriområde i sydöstra Uppsala som ska omvandlas till en ny stadsdel. Med detta har frågan om utnyttjande av redan befintliga byggnadsmaterial i området uppmärksammats. Denna rapport har tagits fram på Uppsala kommuns och STUNS energi:s begäran för att ge förslag på åtgärder för en hög återvinningsgrad vid byggandet av främre Boländerna.  Med hjälp av tekniska beskrivningar bestämdes materialvolymerna av byggmaterialen betong, gips, tegel, trä och stål i fem byggnader i det blivande rivningsområdet. Litteraturstudier gav information om materialen och dagens återvinningsprocesser samt data från livscykelanalyser för de fem olika byggnadsmaterialen. En hållbarhetsmodell konstruerades med kriterier för att kunna jämföra de olika materialen. Denna modell användes som underlag vid bedömningen av huruvida återvinning och återanvändning av de olika materialen var miljömässigt lönsamt och hållbart. I hållbarhetsmodellen ingick även kostnader som ett kriterium. Resultatet visade att återvinning av samtliga material kan leda till miljöbesparingar. Stål visade sig ha hög klimatpåverkan både vid nyproduktion och återvinning, men enligt modellen var återvinning av materialet ändå fördelaktigt utifrån ett miljöperspektiv. Återvinning av trä respektive återanvändning av tegel visade mest hållbara resultat för alla kriterier i modellen utom kostnaden. I rapporten presenteras också ett urval av studier som kan bli relevanta för att i framtiden ytterligare öka återvinnings- eller återanvändningsgraden av materialen. Exempelvis en ny återvinningsteknik för betong som kan användas på rivningsplatsen vid namn Concrete to Cement and Aggregate (C2CA). I studien undersöktes även åtgärder för att minska klimatpåverkan vid bygg- och rivningsprojekt i Uppsala kommun. Det framkom att materialinventering, noggrann planering av avfallshantering och transport samt tydliga styrmedel och miljöcertifieringar kan bidra till ett ökat fokus på hållbarhet inom byggbranschen och därmed minska dess klimatpåverkan.
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Chen, Nai Chun. "Urban data mining : social media data analysis as a complementary tool for urban design." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/106414.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Architecture, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 70-71).
The emergence of "big data" has resulted in a large amount of information documenting daily events, perceptions, thoughts, and emotions of citizens, all annotated with the location and time that they were recorded. This data presents an unprecedented opportunity to help identify and solve urban problems. This thesis aimed to explore the potential of machine learning and data mining in finding patterns in "big" urban data. We explored several different types of user generated urban data, including Call Detail Records (CDR) data and social media (Crunch Base, Yelp, Twitter, and Flickr, and Trip Advisor) data on two primary urban issues. First, we aimed to explore an important 21st century urban problem: how to make successful "Innovative district". Using data mining, we discovered several important characteristics of "innovative districts". Second, we aimed to see if big data is able to help diagnose and alleviate existing problems in cities. For this, we focused on the city of Andorra, and discovered potential reasons for recent declines in tourism in the city. We also discovered that we can learn the travel patterns of tourists to Andorra from their past behavior. In this way, we can predict their future travel plans and help their travels, showing the power of data mining urban data in helping to solve future urban problems as well as diagnose and improve existing problems.
by Nai Chun Chen.
S.M.
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Jiang, Shan Ph D. Massachusetts Institute of Technology. "Deciphering human activities in complex urban systems : mining big data for sustainable urban future." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101369.

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Thesis: Ph. D. in Urban and Regional Planning, Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 187-200).
"Big Data" is in vogue, and the explosion of urban sensors, mobile phone traces, and other windows onto urban activities has generated much hype about the advent of a new 'urban science.' However, translating such Big Data into a planning-relevant understanding of activity patterns and travel behavior presents a number of obstacles. This dissertation examines some of these obstacles and develops data processing pipelines and urban activity modeling techniques that can complement traditional travel surveys and facilitate the development of richer models of activity patterns and land use-transportation interactions. This study develops methods and tests their usefulness by using Singapore metropolitan area as an example, and employing data mining and statistical learning methods to distill useful spatiotemporal information on human activities by people and by place from traditional travel survey data, semantically enriched GIS data, massive and passive call detail records (CDR) data, and Wi-Fi augmented mobile positioning data. I illustrate that regularity and heterogeneity exist in individuals' daily activity patterns in the metropolitan area. I test the hypothesis that by characterizing and clustering individuals' activity profiles, and incorporating them into household decision choice models, we can characterize household lifestyles in ways that enhance our understanding and enable us to predict important decision-making processes within the urban system. I also demonstrate ways of integrating Big Data with traditional data sources in order to identify human mobility patterns, urban structures, and semantic themes of places reflected by human activities. Finally, I discuss how the enriched understanding about cities, human mobility, activity, and behavior choices derived from Big Data can make a difference in land use planning, urban growth management, and transportation policies.
by Shan Jiang.
Ph. D. in Urban and Regional Planning
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Vahedian, Khezerlou Amin. "Mining big mobility data for large urban event analytics." Diss., University of Iowa, 2019. https://ir.uiowa.edu/etd/7039.

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This thesis seeks to formulate concepts and develop methods that facilitate the mining of urban big mobility data. Specifically, the aim of the formulations and developed methods is to identify and predict certain events that occur as a result of urban mobility. This thesis, studies unexpected gathering and dispersal events. A Gathering event is the process of an unusually large number of moving objects (e.g. taxi) arriving at the same area within a short period of time. It is important for city management to identify emerging gathering events which might cause public safety or sustainability concerns. Similarly, a dispersal event is the process of an unusually large number of moving objects leaving the same area within a short period of time. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. This thesis solves the problems of early detection and forecasting of gathering and predicting dispersal events. Prior work to detect gathering events uses undirected patterns which lack the ability to specify the dynamic flow of the traffic and the destination of the gathering. Forecasting gathering events is a predictive approach as apposed to descriptive approaches of detection. This thesis is the first to use destination prediction to forecast gathering events. Moreover, the presented destination prediction technique relaxes independence assumptions of related work and addresses the resulting challenges to achieve superior performance. Literature of dispersal event prediction solves this problem as a taxi demand prediction problem. Those methods aim at predicting the regular pattern and are unable to predict rare events. This thesis presents the SmartEdge Algorithm for early detection of gathering events. SmartEdge outputs a gathering footprint that specifies gathering paths and gathering destination. To forecast gathering events, this thesis presents DH-VIGO, which uses a dynamic hybrid model to forecast rare gathering events ahead of the time. Comprehensive evaluations using real-world datasets demonstrate meaningful results and superior performance compared to baseline methods. To predict dispersal events, this thesis uses a two-stage framework based on survival analysis called DILSA+, to predict the start time of the event and an event demand predictor to predict the volume of the demand in case of a dispersal event. Extensive evaluations on real-world data demonstrate that DILSA+ out-performs baselines and can effectively predict dispersal events.
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Bachir, Danya. "Estimating urban mobility with mobile network geolocation data mining." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLL004/document.

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Dans les prochaines décennies, la circulation et les temps de trajets augmenteront drastiquement en raison du fort taux d'accroissement de la population urbaine. L'augmentation grandissante de la congestion sur les réseaux de transports menace le bon fonctionnement des villes à plusieurs niveaux, tels que le bien-être des citoyens, la santé, l'économie, le tourisme ou la pollution.Ainsi, il est urgent, pour les autorités locales et nationales, de promouvoir l'innovation pour la planification urbaine, à l'aide d'une politique de soutien à l'innovation et de prises de mesures radicales.Pour guider les processus de décisions, il est crucial d'estimer, analyser et comprendre la mobilité urbaine au quotidien.Traditionnellement, les informations sur les déplacements des populations était collectées via des rapports nationaux et locaux, tels que les recensements et les enquêtes. Toutefois, ces derniers ont un coût important, induisant une très faible fréquence de mise-à-jour, ainsi qu'une temporalité restreinte des données.En parallèle, les technologies de l'information et de la communication fournissent une quantité de données de mobilité sans précédent, au jour le jour, toutes catégories de population confondues. En particulier, les téléphones portables accompagnent désormais la majorité des citoyens lors de leurs déplacements et activités du quotidien. Dans cette thèse, nous estimons la mobilité urbaine par l'exploration des données du réseau mobile, qui sont collectées en temps réel, sans coût additionnel, par les opérateurs télécoms. Le traitement des données brutes est non-trivial en raison de leur nature sporadique et de la faible précision spatiale couplée à un bruit complexe.La thèse adresse deux problématiques via un schéma d'apprentissage faiblement supervisé (i.e., utilisant très peu de données labellisées) combinant plusieurs sources de données de mobilité. Dans un premier temps, nous estimons les densités de population et le nombre de visiteurs au cours du temps, à une échelle spatio-temporelle relativement fine.Dans un second temps, nous construisons les matrices Origine-Destination qui représentent les flux totaux de déplacements au cours du temps, pour différents modes de transports.Ces estimations sont validées par une comparaison avec des données de mobilité externes, avec lesquelles de fortes corrélations et de faibles erreurs sont obtenues.Les modèles proposés sont robustes au bruit et à la faible fréquence des données, bien que la performance des modèles soit fortement dépendante de l'échelle spatiale.Pour atteindre une performance optimale, la calibration des modèles doit également prendre en compte la zone d'étude et le mode de transport. Cette étape est nécessaire pour réduire les biais générés par une densité urbaine hétérogène et les différents comportements utilisateur.Ces travaux sont les premiers à estimer les flux totaux de voyageurs routiers et ferrés dans le temps, à l'échelle intra-régionale.Bien qu'une validation plus approfondie des modèles soit requise pour les renforcer, nos résultats mettent en évidence l'énorme potentiel de la science des données de réseaux mobiles appliquées à la planification urbaine
In the upcoming decades, traffic and travel times are expected to skyrocket, following tremendous population growth in urban territories. The increasing congestion on transport networks threatens cities efficiency at several levels such as citizens well-being, health, economy, tourism and pollution. Thus, local and national authorities are urged to promote urban planning innovation by adopting supportive policies leading to effective and radical measures. Prior to decision making processes, it is crucial to estimate, analyze and understand daily urban mobility. Traditionally, the information on population movements has been gathered through national and local reports such as census and surveys. Still, such materials are constrained by their important cost, inducing extremely low-update frequency and lack of temporal variability. On the meantime, information and communications technologies are providing an unprecedented quantity of up-to-date mobility data, across all categories of population. In particular, most individuals carry their mobile phone everywhere through their daily trips and activities. In this thesis, we estimate urban mobility by mining mobile network data, which are collected in real-time by mobile phone providers at no extra-cost. Processing the raw data is non-trivial as one must deal with temporal sparsity, coarse spatial precision and complex spatial noise. The thesis addresses two problematics through a weakly supervised learning scheme (i.e., using few labeled data) combining several mobility data sources. First, we estimate population densities and number of visitors over time, at fine spatio-temporal resolutions. Second, we derive Origin-Destination matrices representing total travel flows over time, per transport modes. All estimates are exhaustively validated against external mobility data, with high correlations and small errors. Overall, the proposed models are robust to noise and sparse data yet the performance highly depends on the choice of the spatial resolution. In addition, reaching optimal model performance requires extra-calibration specific to the case study region and to the transportation mode. This step is necessary to account for the bias induced by the joined effect of heterogeneous urban density and user behavior. Our work is the first successful attempt to characterize total road and rail passenger flows over time, at the intra-region level.Although additional in-depth validation is required to strengthen this statement, our findings highlight the huge potential of mobile network data mining for urban planning applications
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Kwon, Jongwan. "Mining Manhattan : a new urban model for recycling electronic waste." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103471.

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Thesis: M. Arch., Massachusetts Institute of Technology, Department of Architecture, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 124-125).
This thesis proposes an electronic waste recycling center in downtown Manhattan as the test site for a new ecosystem of material production and consumption. Discarded electronic materials represent the single fastest growing source of municipal waste, which is often illegally exported to developing countries such as Ghana, Nigeria, India, China before being processed into reusable materials. As urban societies increasingly rely on digital devices, and those devices become obsolete at rapid rates, a new model for managing e-waste is desperately needed. The thesis employs architecture to raise awareness, illuminate deficiencies in the current model of e-waste management, and orchestrate an alternative model to current practices. The project is situated on the Gansevoort peninsula on the west side of Manhattan on a wasteland made from landfill, and the former site of a municipal waste incinerator. Micro collection points throughout the island collect approximately 100 tons of daily e-waste that are then transported to the recycling center, which serves the entire island. The architecture transforms e-waste into commodifiable resources such as gold and silver to make new products. Not only is the architecture a machine for creating new material but it becomes a site for exchanging knowledge, allowing public to engage and participate with the recycling processes. By exploiting the site's latent symbolic and logistical value, this thesis proposes a new urban consumption cycle. "One man's trash is another man's treasure"; obsolete devices enjoy their second lives.
by Jongwan Kwon.
M. Arch.
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Books on the topic "Urban Mining"

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Nakamura, Takashi, and Kohmei Halada. Urban Mining Systems. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-55075-4.

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Oursler, Anna. Mining Urban Heat. [New York, N.Y.?]: [publisher not identified], 2015.

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Ghosh, Sadhan Kumar, ed. Urban Mining and Sustainable Waste Management. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0532-4.

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Smith, Duane A. Rocky Mountain mining camps: The urban frontier. Niwot, Colo: University Press of Colorado, 1992.

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Mining urban wastes: The potential for recycling. Washington, D.C., USA: Worldwatch Institute, 1987.

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Pathak, Pankaj, and Prangya Ranjan Rout. Urban Mining for Waste Management and Resource Recovery. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003201076.

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L, Jones Philip, and Joplin Historical Society, eds. Joplin: From mining town to urban center : an illustrated history. Northridge, Calif: Windsor Publications, 1985.

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The remaking of the mining industry. Houndmills, Basingstoke, Hampshire: Palgrave Macmillan, 2015.

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Behnisch, Martin. Urban Data Mining: Operationalisierung der Strukturerkennung und Strukturbildung von Ähnlichkeitsmustern über die gebaute Umwelt. Karlsruhe: Univ.-Verl. Karlsruhe, 2008.

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Lindsey, David A. An introduction to sand and gravel deposit models, Front Range urban corridor. [Reston, Va.?]: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Book chapters on the topic "Urban Mining"

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Qi, Jianguo, Jingxing Zhao, Wenjun Li, Xushu Peng, Bin Wu, and Hong Wang. "“Urban Mining”." In Research Series on the Chinese Dream and China’s Development Path, 247–74. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-2466-5_12.

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Nakamura, Takashi, and Kohmei Halada. "Potential of Urban Mine." In Urban Mining Systems, 7–29. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55075-4_2.

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Nakamura, Takashi, and Kohmei Halada. "Development of Urban Mine." In Urban Mining Systems, 31–45. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55075-4_3.

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Zhang, Chao, and Jiawei Han. "Data Mining and Knowledge Discovery." In Urban Informatics, 797–814. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_42.

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AbstractOur physical world is being projected into online cyberspace at an unprecedented rate. People nowadays visit different places and leave behind them million-scale digital traces such as tweets, check-ins, Yelp reviews, and Uber trajectories. Such digital data are a result of social sensing: namely people act as human sensors that probe different places in the physical world and share their activities online. The availability of massive social-sensing data provides a unique opportunity for understanding urban space in a data-driven manner and improving many urban computing applications, ranging from urban planning and traffic scheduling to disaster control and trip planning. In this chapter, we present recent developments in data-mining techniques for urban activity modeling, a fundamental task for extracting useful urban knowledge from social-sensing data. We first describe traditional approaches to urban activity modeling, including pattern discovery methods and statistical models. Then, we present the latest developments in multimodal embedding techniques for this task, which learns vector representations for different modalities to model people's spatiotemporal activities. We study the empirical performance of these methods and demonstrate how data-mining techniques can be successfully applied to social-sensing data to extract actionable knowledge and facilitate downstream applications.
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Nakamura, Takashi, and Kohmei Halada. "Introduction." In Urban Mining Systems, 1–6. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55075-4_1.

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Nakamura, Takashi, and Kohmei Halada. "Summary." In Urban Mining Systems, 47. Tokyo: Springer Japan, 2014. http://dx.doi.org/10.1007/978-4-431-55075-4_4.

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Mettke, Angelika, Viktoria Arnold, and Stephanie Schmidt. "Erste Schritte zum Urban Mining." In Aktuelle Ansätze zur Umsetzung der UN-Nachhaltigkeitsziele, 113–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-58717-1_7.

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Haldorai, Anandakumar, Arulmurugan Ramu, and Suriya Murugan. "Web Intelligence and Data Mining in Urban Areas." In Urban Computing, 27–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26013-2_2.

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Dao, Minh-Son, R. Uday Kiran, and Koji Zettsu. "Insights for Urban Road Safety: A New Fusion-3DCNN-PFP Model to Anticipate Future Congestion from Urban Sensing Data." In Periodic Pattern Mining, 237–63. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3964-7_14.

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Wang, Jiazhuo G., and Juan Yang. "Urban Mining: The Story of GEM." In Who Gets Funds from China’s Capital Market?, 19–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-44913-0_3.

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Conference papers on the topic "Urban Mining"

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Koutamanis, Alexander, Boukje Van Reijn, and Ellen Van Bueren. "Anticipating urban mining." In 24th Annual European Real Estate Society Conference. European Real Estate Society, 2017. http://dx.doi.org/10.15396/eres2017_70.

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Nikolopoulos, Spiros, Symeon Papadopoulos, and Yiannis Kompatsiaris. "Reality mining in urban space." In 2013 Fourth International Conference on Information, Intelligence, Systems and Applications (IISA). IEEE, 2013. http://dx.doi.org/10.1109/iisa.2013.6623711.

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Yuan, Nicholas Jing. "Mining Social and Urban Big Data." In WWW '15: 24th International World Wide Web Conference. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2740908.2745843.

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Wei, Xian, Huaiyong Shao, Wancun Zhou, Jieming Zhou, Jie Huang, and Liuzhi. "Eco-security evaluation in Panxi mining concentrated area." In 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137612.

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Liu Guang, Guo Huadong, Fan jinghui, Guo Xiaofang, Zbigniew Perski, and Yue Huanyin. "Mining area subsidence monitoring using multi-band SAR data." In 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137665.

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Jianguo He, Guang Liu, and Huanyin Yue. "Monitoring ground subsidence in mining area using spaceborne InSAR technology." In 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137668.

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Green, Ben, Alejandra Caro, Matthew Conway, Robert Manduca, Tom Plagge, and Abby Miller. "Mining Administrative Data to Spur Urban Revitalization." In KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2783258.2788568.

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Zhao, Kai, Sasu Tarkoma, Siyuan Liu, and Huy Vo. "Urban human mobility data mining: An overview." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840811.

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Shao, Huaiyong, Wei Xian, Wunian Yang, Jiec Huang, and Zhi Liu. "Ecological environment quality evaluation in mining city A case of Panzhihua." In 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137624.

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Han, Dong, and Chunhua Wang. "Data Prediction Based on Data Mining Combined Model." In Security-enriched Urban Computing and Smart Grids 2016. Science & Engineering Research Support soCiety, 2016. http://dx.doi.org/10.14257/astl.2016.137.13.

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Reports on the topic "Urban Mining"

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Савосько, Василь Миколайович, Наталія Вікторівна Товстоляк, Юрій Васильович Лихолат, and Іван Панасович Григорюк. Structure and Diversity of Urban Park Stands at Kryvyi Rih Ore-Mining & Metallurgical District, Central Ukraine. Podgorica, 2020. http://dx.doi.org/10.31812/123456789/3946.

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The present study examines the relationships between structure (floristic composition, dendrometric parameters), diversity (diversity and evenness indexes) of urban forest park stands and the ecological (soil fertility, soil moisture), environmental factors (air pollution). The study is based on the forest park stands inventory data, performed from 2012 to 2017 in Kryvyi Rih City, Central Ukraine. The floristic compositions of the urban forest park stands are poor. There are only 23 species that belong to 14 families and 12 genera. More families were represented by at least more than 2% of taxon diversity. While Ulmaceae (2 genera, 4 species–17,39 %), Fabaceae (3 genera, 2 species–17,39 %), Aсеrасеае (1 genera, 4 species–17,39 %) were the most representative families. It was established that at forest park the values of stand density varied from 490 to 660 trees*ha-1, stem heights were from 26 to 31 m, stem diameters were from 13 to 17 cm, stand basal area were from 32 to 49 m2*ha-1, stand volume were from 200 to 415 m3*ha-1. the values of relative stem heights were from 0,63 to 0,82 m*year-1, relative stem diameters were from 0,31 to 0,43 cm*year-1, relative stand basal area were from 0,80 to 1,19 m2*ha-1*year-1, relative stand volume were from 5,45 to 10,28 m3*ha-1*year-1. The varied values of the forest park stands index (Shannon-Wiener diversity index from 0,75 to 1,61, Pielou‟s evenness index from 0,53 to 0,86, Simpson‟s diversity from 0,24 to 0,60, Margalef‟s diversity index from 0,87 to 6,97) indicate the ecological instability of these woody plant communities. Current state of the urban forest park stands determined by the combined influence of ecological (soil fertility, soil moisture) and environmental factors (air pollution).
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Riggs, William, Vipul Vyas, and Menka Sethi. Blockchain and Distributed Autonomous Community Ecosystems: Opportunities to Democratize Finance and Delivery of Transport, Housing, Urban Greening and Community Infrastructure. Mineta Transportation Institute, July 2022. http://dx.doi.org/10.31979/mti.2022.2165.

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This report investigates and develops specifications for using blockchain and distributed organizations to enable decentralized delivery and finance of urban infrastructure. The project explores use cases, including: providing urban greening, street or transit infrastructure; services for street beautification, cleaning and weed or graffiti abatement; potential ways of resource allocation ADU; permitting and land allocation; and homeless housing. It establishes a general process flow for this blockchain architecture, which involves: 1) the creation of blocks (transactions); 2) sending these blocks to nodes (users) on the network for an action (mining) and then validation that that action has taken place; and 3) then adding the block to the blockchain. These processes involve the potential for creating new economic value for cities and neighborhoods through proof-of-work, which can be issued through a token (possibly a graphic non-fungible token), certificate, or possible financial reward. We find that encouraging trading of assets at the local level can enable the creation of value that could be translated into sustainable “mining actions” that could eventually provide the economic backstop and basis for new local investment mechanisms or currencies (e.g., local cryptocurrency). These processes also provide an innovative local, distributed funding mechanism for transportation, housing and other civic infrastructure.
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Muelaner, Jody Emlyn. Decarbonized Power Options for Non-road Mobile Machinery. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, January 2023. http://dx.doi.org/10.4271/epr2023002.

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<div class="section abstract"><div class="htmlview paragraph">Power options for off-road vehicles differ substantially from other commercial vehicles. Battery electrification is suitable for urban construction and light agriculture, but remote mining, forestry, and road building operations will require alternative fuels.</div><div class="htmlview paragraph"><b>Decarbonized Power Options for Non-road Mobile Machinery</b> discusses these domains as well as the potential benefits and challenges of implementing fuels and energy sources such as bioenergy, e-fuels, and alcohol, as well as hydrogen, hydrocarbon, and direct methanol fuel cells.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>
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Voss, Hank D., and Jeff Dailey. EyePod-Mini: Constellations, Urban Launches and Buoy Landings. Ames (Iowa): Iowa State University. Library. Digital Press, January 2015. http://dx.doi.org/10.31274/ahac.9761.

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