81 datasets found
  1. e

    GIS

    • paper.erudition.co.in
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    Updated May 11, 2023
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    Einetic (2023). GIS [Dataset]. https://paper.erudition.co.in/makaut/btech-in-civil-engineering/8/gis-and-remote-sensing
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    htmlAvailable download formats
    Dataset updated
    May 11, 2023
    Dataset authored and provided by
    Einetic
    License

    https://paper.erudition.co.in/termshttps://paper.erudition.co.in/terms

    Description

    Question Paper Solutions of chapter GIS of GIS & Remote Sensing, 8th Semester , Civil Engineering

  2. A Geospatial and Binomial Logistic Regression Model to Prioritize Sampling...

    • figshare.com
    • resodate.org
    zip
    Updated Jul 29, 2022
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    Sweta Ojha; Kelly Pennell; Ariel Robinson; Nader Rezaei; Anna Hoover; Ying Li; Christian Powell; Hunter Moseley; Patrick Thompson (2022). A Geospatial and Binomial Logistic Regression Model to Prioritize Sampling for Per- and Polyfluorinated Alkyl Substances (PFAS) in Public Water Systems-Dataset [Dataset]. http://doi.org/10.6084/m9.figshare.16560144.v5
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    zipAvailable download formats
    Dataset updated
    Jul 29, 2022
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Sweta Ojha; Kelly Pennell; Ariel Robinson; Nader Rezaei; Anna Hoover; Ying Li; Christian Powell; Hunter Moseley; Patrick Thompson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    IIt includes data that were used in the manuscript(A Geospatial and Binomial Logistic Regression Model to Prioritize Sampling for Per- and Polyfluorinated Alkyl Substances (PFAS) in Public Water Systems.) It includes layers that were created in online ArcGIS pro in manuscript and result of regression model that was done in the manuscript.

  3. ArcGIS Training in Nepal

    • kaggle.com
    zip
    Updated Sep 22, 2024
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    Tek Bahadur Kshetri (2024). ArcGIS Training in Nepal [Dataset]. https://www.kaggle.com/datasets/tekbahadurkshetri/arcgis-training-in-nepal
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    zip(571304278 bytes)Available download formats
    Dataset updated
    Sep 22, 2024
    Authors
    Tek Bahadur Kshetri
    Area covered
    Nepal
    Description

    The Civil Engineering Students Society organized an 'ArcGIS Online Training for Beginners.' Geographical Information System (GIS) technology provides the tools for creating, managing, analyzing, and visualizing data associated with developing and managing infrastructure.

    It also allowed civil engineers to manage and share data, turning it into easily understood reports and visualizations that could be analyzed and communicated to others. Additionally, it helped civil engineers in spatial analysis, data management, urban development, town planning, and site analysis.

    It is equally important for beginner geospatial students.

  4. e

    GIS & Remote Sensing (CE(PE)801A), 8th Semester, Civil Engineering, MAKAUT |...

    • paper.erudition.co.in
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    Updated May 11, 2023
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    Einetic (2023). GIS & Remote Sensing (CE(PE)801A), 8th Semester, Civil Engineering, MAKAUT | Erudition Paper [Dataset]. https://paper.erudition.co.in/makaut/btech-in-civil-engineering/8/gis-and-remote-sensing
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    htmlAvailable download formats
    Dataset updated
    May 11, 2023
    Dataset authored and provided by
    Einetic
    License

    https://paper.erudition.co.in/termshttps://paper.erudition.co.in/terms

    Description

    Question Paper Solutions of GIS & Remote Sensing (CE(PE)801A),8th Semester,Civil Engineering,Maulana Abul Kalam Azad University of Technology

  5. m

    Data from: A GIS PROTOCOL FOR ENHANCING THE SELECTION OF AGRICULTURAL RUNOFF...

    • data.mendeley.com
    Updated May 9, 2022
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    Luke Kehoe (2022). A GIS PROTOCOL FOR ENHANCING THE SELECTION OF AGRICULTURAL RUNOFF SAMPLING LOCATIONS AND PREDICTING THE LOCATIONS OF POTENTIAL POLLUTANT TRANSPORT IN THE UPLAND ENVIRONMENT [Dataset]. http://doi.org/10.17632/wdjzftxyfd.1
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    Dataset updated
    May 9, 2022
    Authors
    Luke Kehoe
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This study presents an ArcGIS geoprocessing protocol for quickly processing large amounts of data from publicly available government sources to consider both water quality standards (WQS) and nonpoint pollution source (NPS) control, on a watershed-by-watershed basis to administratively predict locations where nonpoint source pollutants may contribute to the impairment of downstream waters and locations where nonpoint source pollutants are not expected to contribute to the impairment of downstream waters. This dissertation also presents an ArcGIS geoprocessing protocol to calculate the hydrological response time of a watershed and to predict the potential for soil erosion and nonpoint source pollutant movement on a landscape scale. The standardized methodologies employed by the protocol allow for its use in various geographic regions. The methodology has been performed on sites in Linn County and Boone County, Missouri, and produces results consistent with those expected from other widely accepted methods. These protocols were developed studying the movement of atrazine. but may be used for various nonpoint source pollutants that are water soluble, have an affinity to soil binding, and associated with a particular land use. All data and code are available in Mendeley Data (doi: 10.17632/wdjzftxyfd.1).

  6. r

    GISF2E: ArcGIS, QGIS, and python tools and Tutorial

    • resodate.org
    • figshare.com
    Updated Jan 1, 2017
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    Urban Road Networks (2017). GISF2E: ArcGIS, QGIS, and python tools and Tutorial [Dataset]. http://doi.org/10.6084/M9.FIGSHARE.2065320
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    Dataset updated
    Jan 1, 2017
    Dataset provided by
    figshare
    Authors
    Urban Road Networks
    Description

    ArcGIS tool and tutorial to convert the shapefiles into network format. The latest version of the tool is available at http://csun.uic.edu/codes/GISF2E.html Update: we now have added QGIS and python tools. To download them and learn more, visit http://csun.uic.edu/codes/GISF2E.html Please cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

  7. e

    Database and Coordinate System

    • paper.erudition.co.in
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    Updated May 11, 2023
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    Einetic (2023). Database and Coordinate System [Dataset]. https://paper.erudition.co.in/makaut/btech-in-civil-engineering/8/gis-and-remote-sensing
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    htmlAvailable download formats
    Dataset updated
    May 11, 2023
    Dataset authored and provided by
    Einetic
    License

    https://paper.erudition.co.in/termshttps://paper.erudition.co.in/terms

    Description

    Question Paper Solutions of chapter Database and Coordinate System of GIS & Remote Sensing, 8th Semester , Civil Engineering

  8. Urban Road Network Data

    • figshare.com
    • resodate.org
    zip
    Updated May 30, 2023
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    Urban Road Networks (2023). Urban Road Network Data [Dataset]. http://doi.org/10.6084/m9.figshare.2061897.v1
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    zipAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Urban Road Networks
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Tool and data set of road networks for 80 of the most populated urban areas in the world. The data consist of a graph edge list for each city and two corresponding GIS shapefiles (i.e., links and nodes).Make your own data with our ArcGIS, QGIS, and python tools available at: http://csun.uic.edu/codes/GISF2E.htmlPlease cite: Karduni,A., Kermanshah, A., and Derrible, S., 2016, "A protocol to convert spatial polyline data to network formats and applications to world urban road networks", Scientific Data, 3:160046, Available at http://www.nature.com/articles/sdata201646

  9. a

    eBook: Lindsey the GIS Specialist

    • edu.hub.arcgis.com
    Updated Mar 26, 2019
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    Education and Research (2019). eBook: Lindsey the GIS Specialist [Dataset]. https://edu.hub.arcgis.com/documents/4915f2532b1144089914b04dc544800a
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    Dataset updated
    Mar 26, 2019
    Dataset authored and provided by
    Education and Research
    Area covered
    Description

    Bolton & Menk, an engineering planning and consulting firm from the Midwestern United States has released a series of illustrated children’s books as a way of helping young people discover several different professions that typically do not get as much attention as other more traditional ones do.Topics of the award winning book series include landscape architecture, civil engineering, water resource engineering, urban planning and now Geographic Information Systems (GIS). The books are available free online in digital format, and easily accessed via a laptop, smart phone or tablet.The book Lindsey the GIS Specialist – A GIS Mapping Story Tyler Danielson, covers some the basics of what geographic information is and the type of work that a GIS Specialist does. It explains what the acronym GIS means, the different types of geospatial data, how we collect data, and what some of the maps a GIS Specialist creates would be used for.Click here to check out the GIS Specialist – A GIS Mapping Story e-book

  10. Civil Engineering Market Analysis APAC, North America, Europe, Middle East...

    • technavio.com
    pdf
    Updated Nov 26, 2024
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    Technavio (2024). Civil Engineering Market Analysis APAC, North America, Europe, Middle East and Africa, South America - China, US, India, Germany, Canada - Size and Forecast 2024-2028 [Dataset]. https://www.technavio.com/report/civil-engineering-market-industry-analysis
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    pdfAvailable download formats
    Dataset updated
    Nov 26, 2024
    Dataset provided by
    TechNavio
    Authors
    Technavio
    License

    https://www.technavio.com/content/privacy-noticehttps://www.technavio.com/content/privacy-notice

    Time period covered
    2024 - 2028
    Area covered
    Canada, United States
    Description

    Snapshot img

    Civil Engineering Market Size 2024-2028

    The civil engineering market size is forecast to increase by USD 2.57 billion at a CAGR of 3.9% between 2023 and 2028.

    The market is experiencing significant growth, driven by the surge in construction activities in developing countries. This trend is expected to continue as infrastructure development remains a priority for many governments. Another key factor fueling market growth is the adoption of intelligent processing in civil engineering projects. This includes the use of technologies such as Building Information Modeling (BIM) and Geographic Information Systems (GIS) to improve project efficiency and accuracy. 
    However, the market is also facing challenges, including the decline in construction activities in some regions due to economic downturns and natural disasters. Despite these challenges, the future of the market looks promising, with continued investment in infrastructure development and the ongoing integration of advanced technologies.
    

    What will be the Size of the Civil Engineering Market During the Forecast Period?

    Request Free Sample

    The civil engineering services market encompasses a broad range of construction activities, including social infrastructure, residential, offices, educational institutes, luxury hotels, restaurants, transport buildings, online retail warehousing, and various types of infrastructure projects such as roads, bridges, railroads, airports, and ports. This market is driven by various factors, including population growth, urbanization, and the increasing demand for sustainable and energy-efficient structures. 
    Digitalization plays a significant role In the civil engineering sector, with the adoption of digital civil engineering, smart grids, urban transportation systems, industrial automation, parking systems, and IT services. Additionally, there is a growing trend towards the development of zero-energy buildings, insulated buildings, double skin facades, PV panels, and e-permit systems.
    Inspection technology and integrated 3D modeling are also becoming increasingly important In the civil engineering industry, enabling more accurate and efficient design and construction processes. The market is expected to continue growing, driven by the increasing demand for infrastructure development and the ongoing digital transformation of the industry.
    

    How is this Civil Engineering Industry segmented and which is the largest segment?

    The civil engineering industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

    Application
    
      Real estate
      Infrastructure
      Industrial
    
    
    Geography
    
      APAC
    
        China
        India
    
    
      North America
    
        Canada
        US
    
    
      Europe
    
        Germany
    
    
      Middle East and Africa
    
    
    
      South America
    

    By Application Insights

    The real estate segment is estimated to witness significant growth during the forecast period. The real estate market encompasses the development, acquisition, and sale of property, land, and buildings. Global urbanization and infrastructure investment growth have significantly impacted this sector. In particular, the Asia Pacific region has seen rapid expansion in various sectors, such as commercial construction, with India leading the charge. Notably, international real estate development is projected to present opportunities for countries like India, as demonstrated by the October 2021 MoU between the Jammu and Kashmir administration and the Dubai government, focusing on industrial parks, IT towers, and super-specialty hospitals. Civil engineering services play a crucial role in real estate development, with a focus on social infrastructure, residential, construction activities, offices, educational institutes, hotels, restaurants, transport buildings, online retail warehousing, immigration, housing, and construction.

    Innovations in green building products, energy efficiency, sustainable construction materials, such as cross-laminated timber, and digital technology are transforming the industry. Key areas of growth include infrastructure, oil and gas, energy and power, aviation, public spending, non-residential construction, healthcare centers, infrastructure projects, and digital civil engineering. Civil engineering firms provide essential services, including rail structures, tunnels, bridges, maintenance services, renovation activities, and energy-efficient products. The real estate segment also includes industrial real estate and housing development, with a shift towards flexible infrastructure, roads, railroads, airports, ports, single-family houses, and home remodeling. The industry is embracing advanced simulation tools, drone technology, and carbon emissions reduction initiatives, such as net-zero energy buildings, pre-fabrica

  11. Transportation & Civil Engineers for Tomorrow

    • planningdivisiongis-nmdot.hub.arcgis.com
    Updated Jul 7, 2023
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    NMDOT ArcGIS Online (2023). Transportation & Civil Engineers for Tomorrow [Dataset]. https://planningdivisiongis-nmdot.hub.arcgis.com/items/6d0e0fdca9e547d5affeffb46d78932b
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    Dataset updated
    Jul 7, 2023
    Dataset provided by
    New Mexico Department of Transportationhttps://www.dot.nm.gov/
    Authors
    NMDOT ArcGIS Online
    Description

    The American Association of State Highways and Transportation Officials (AASHTO) STEM Outreach Solutions program is a hands-on educational outreach program for use in Science Technology, Engineering, and Math (STEM) classes for grades K-12th. There are uniquely designed modules to supply teachers with the needed materials to engage students in solving real-world problems to improve math and science skills in elementary, middle, and high school classes. The curriculum supports national Common Core standards and improves work force diversity which helps to resolve one of the most pressing problems faced by the transportation today: a critical shortage of civil engineers.Department of Transportations (DOTs) partner with schools by providing the curricula, resources, and trainings at no charge to the schools. The NMDOT is implementing at several schools in New Mexico. NMDOT engineers and staff offer their support in the classrooms by demonstrating how the activities work, helping with the projects, and providing role models who can answer questions about transportation careers. Their involvement and leadership can help students understand the connections between concepts learned in the classroom and how those concepts are applied in practice.

  12. D

    Field Data Collection Apps For Civil Engineering Market Research Report 2033...

    • dataintelo.com
    csv, pdf, pptx
    Updated Sep 30, 2025
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    Dataintelo (2025). Field Data Collection Apps For Civil Engineering Market Research Report 2033 [Dataset]. https://dataintelo.com/report/field-data-collection-apps-for-civil-engineering-market
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    csv, pdf, pptxAvailable download formats
    Dataset updated
    Sep 30, 2025
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Field Data Collection Apps for Civil Engineering Market Outlook



    According to our latest research, the Field Data Collection Apps for Civil Engineering market size reached USD 1.45 billion in 2024 and is expected to grow at a robust CAGR of 13.8% during the forecast period, reaching a projected value of USD 4.11 billion by 2033. This dynamic growth is primarily driven by increasing digitalization in the civil engineering sector, the need for real-time data acquisition, and the growing emphasis on project efficiency and compliance. As per our analysis, the market is experiencing accelerated adoption due to the rising demand for accurate field data, streamlined workflows, and integration with advanced analytics platforms.




    One of the primary growth factors for the Field Data Collection Apps for Civil Engineering market is the rapid digital transformation across the construction and engineering industries. The adoption of mobile technologies and smart devices on job sites has enabled civil engineers to collect, analyze, and transmit data in real time, significantly reducing manual errors and paperwork. The increasing complexity of civil infrastructure projects, combined with the need for precise data to ensure safety and regulatory compliance, has further fueled the demand for field data collection apps. These solutions empower project teams to collaborate seamlessly, enhance productivity, and maintain up-to-date records, which are essential for timely project delivery and cost control.




    Another significant driver is the integration of field data collection apps with other digital platforms such as Building Information Modeling (BIM), Geographic Information Systems (GIS), and cloud-based project management tools. This interoperability allows for the seamless flow of information between field teams and office-based stakeholders, enhancing decision-making and reducing project delays. The ability to capture geospatial data, photographic evidence, and inspection results directly from the field and sync them with centralized databases has become a critical requirement for modern civil engineering projects. Moreover, the increasing emphasis on sustainability and resource optimization is pushing organizations to leverage digital tools that provide actionable insights from field data, further propelling market growth.




    The proliferation of government regulations and industry standards mandating accurate documentation and traceability in civil engineering projects is also contributing to the expansion of the Field Data Collection Apps for Civil Engineering market. Regulatory bodies are increasingly requiring project documentation to be digital, auditable, and easily accessible, which has led to widespread adoption of advanced field data collection solutions. Additionally, the rising focus on infrastructure modernization in emerging economies, coupled with substantial investments in smart city initiatives, is creating new growth opportunities. The demand for scalable, customizable, and secure data collection platforms is expected to remain strong as the civil engineering sector continues to embrace digital transformation.




    Regionally, North America holds the largest market share in 2024, driven by the presence of leading construction technology providers, high adoption rates of digital tools, and stringent regulatory frameworks. Europe follows closely, with significant investments in infrastructure renewal and sustainability initiatives. The Asia Pacific region is experiencing the fastest growth, fueled by rapid urbanization, government-led infrastructure projects, and increasing awareness of the benefits of digital field data collection. Latin America and the Middle East & Africa are also witnessing steady growth, supported by modernization efforts and the gradual adoption of digital construction practices.



    Component Analysis



    The Field Data Collection Apps for Civil Engineering market is segmented by component into software and services, each playing a pivotal role in shaping the market landscape. The software segment dominates the market, accounting for the largest revenue share in 2024. This dominance is attributed to the increasing demand for intuitive, feature-rich applications that enable real-time data capture, analysis, and reporting. Modern field data collection software offers functionalities such as offline data entry, GPS integration, photo capture, and automated synchronization with central databases. The continuous evolution

  13. h

    Civil Engineering Software Market - Global Industry Size & Growth Analysis...

    • htfmarketinsights.com
    pdf & excel
    Updated Oct 15, 2025
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    HTF Market Intelligence (2025). Civil Engineering Software Market - Global Industry Size & Growth Analysis 2019-2031 [Dataset]. https://htfmarketinsights.com/report/2831997-civil-engineering-software-market
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    pdf & excelAvailable download formats
    Dataset updated
    Oct 15, 2025
    Dataset authored and provided by
    HTF Market Intelligence
    License

    https://www.htfmarketinsights.com/privacy-policyhttps://www.htfmarketinsights.com/privacy-policy

    Time period covered
    2019 - 2031
    Area covered
    Global
    Description

    Global Civil Engineering Software Market is segmented by Application (Construction_ infrastructure projects_ urban planning_ transportation_ energy), Type (CAD software_ BIM tools_ simulation software_ project management tools_ GIS software), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

  14. e

    Spatial Data Analysis

    • paper.erudition.co.in
    html
    Updated May 11, 2023
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    Einetic (2023). Spatial Data Analysis [Dataset]. https://paper.erudition.co.in/makaut/btech-in-civil-engineering/8/gis-and-remote-sensing
    Explore at:
    htmlAvailable download formats
    Dataset updated
    May 11, 2023
    Dataset authored and provided by
    Einetic
    License

    https://paper.erudition.co.in/termshttps://paper.erudition.co.in/terms

    Description

    Question Paper Solutions of chapter Spatial Data Analysis of GIS & Remote Sensing, 8th Semester , Civil Engineering

  15. GIS-baserad Tidsmodell. Göteborg, 1960-2015. Buildings

    • search.datacite.org
    Updated 2020
    + more versions
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    Ioanna Stavroulaki; Lars Marcus; Meta Berghauser Pont; Ehsan Abshirini; Jan Sahlberg; Alice Örnö Ax (2020). GIS-baserad Tidsmodell. Göteborg, 1960-2015. Buildings [Dataset]. http://doi.org/10.5878/t8s9-6y15
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    Dataset updated
    2020
    Dataset provided by
    DataCitehttps://www.datacite.org/
    Chalmers University of Technology
    Authors
    Ioanna Stavroulaki; Lars Marcus; Meta Berghauser Pont; Ehsan Abshirini; Jan Sahlberg; Alice Örnö Ax
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Area covered
    Dataset funded by
    Älvstranden Utveckling AB, Fusion Point Gothenburg
    Description

    The GIS-based Time model of Gothenburg aims to map the process of urban development in Gothenburg since 1960 and in particular to document the changes in the spatial form of the city - streets, buildings and plots - through time. Major steps have in recent decades been taken when it comes to understanding how cities work. Essential is the change from understanding cities as locations to understanding them as flows (Batty 2013)1. In principle this means that we need to understand locations (or places) as defined by flows (or different forms of traffic), rather than locations only served by flows. This implies that we need to understand the built form and spatial structure of cities as a system, that by shaping flows creates a series of places with very specific relations to all other places in the city, which also give them very specific performative potentials. It also implies the rather fascinating notion that what happens in one place is dependent on its relation to all other places (Hillier 1996)2. Hence, to understand the individual place, we need a model of the city as a whole. Extensive research in this direction has taken place in recent years, that has also spilled over to urban design practice, not least in Sweden, where the idea that to understand the part you need to understand the whole is starting to be established. With the GIS-based Time model for Gothenburg that we present here, we address the next challenge. Place is not only something defined by its spatial relation to all other places in its system, but also by its history, or its evolution over time. Since the built form of the city changes over time, often by cities growing but at times also by cities shrinking, the spatial relation between places changes over time. If cities tend to grow, and most often by extending their periphery, it means that most places get a more central location over time. If this is a general tendency, it does not mean that all places increase their centrality to an equal degree. Depending on the structure of the individual city’s spatial form, different places become more centrally located to different degrees as well as their relative distance to other places changes to different degrees. The even more fascinating notion then becomes apparent; places move over time! To capture, study and understand this, we need a "time model". The GIS-based time model of Gothenburg consists of: • 12 GIS-layers of the street network, from 1960 to 2015, in 5-year intervals • 12 GIS-layers of the buildings from 1960 to 2015, in 5-year intervals • 12 GIS- layers of the plots from1960 to 2015, in 5-year intervals In the GIS-based Time model, for every time-frame, the combination of the three fundamental components of spatial form, that is streets, plots and buildings, provides a consistent description of the built environment at that particular time. The evolution of three components can be studied individually, where one could for example analyze the changing patterns of street centrality over time by focusing on the street network; or, the densification processes by focusing on the buildings; or, the expansion of the city by way of occupying more buildable land, by focusing on plots. The combined snapshots of street centrality, density and land division can provide insightful observations about the spatial form of the city at each time-frame; for example, the patterns of spatial segregation, the distribution of urban density or the patterns of sprawl. The observation of how the interrelated layers of spatial form together evolved and transformed through time can provide a more complete image of the patterns of urban growth in the city. The Time model was created following the principles of the model of spatial form of the city, as developed by the Spatial Morphology Group (SMoG) at Chalmers University of Technology, within the three-year research project ‘International Spatial Morphology Lab (SMoL)’. The project is funded by Älvstranden Utveckling AB in the framework of a larger cooperation project called Fusion Point Gothenburg. The data is shared via SND to create a research infrastructure that is open to new study initiatives. 1. Batty, M. (2013), The New Science of Cities, Cambridge: MIT Press. 2. Hillier, B., (1996), Space Is the Machine. Cambridge: University of Cambridge

  16. D

    Civil Engineering Market Report | Global Forecast From 2025 To 2033

    • dataintelo.com
    csv, pdf, pptx
    Updated Oct 16, 2024
    + more versions
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    Dataintelo (2024). Civil Engineering Market Report | Global Forecast From 2025 To 2033 [Dataset]. https://dataintelo.com/report/global-civil-engineering-market
    Explore at:
    pptx, csv, pdfAvailable download formats
    Dataset updated
    Oct 16, 2024
    Dataset authored and provided by
    Dataintelo
    License

    https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy

    Time period covered
    2024 - 2032
    Area covered
    Global
    Description

    Civil Engineering Market Outlook



    The global civil engineering market size was valued at approximately $9.7 trillion in 2023 and is projected to reach nearly $14.6 trillion by 2032, growing at a compound annual growth rate (CAGR) of 4.5% during the forecast period. This substantial growth is driven by increasing urbanization, infrastructure development, and investments in residential and commercial projects worldwide. The burgeoning demand for sustainable construction practices and innovative engineering solutions is further bolstering market expansion.



    One of the primary growth factors of the civil engineering market is the rapid pace of urbanization. As more people move into urban areas, the demand for new housing, transportation systems, utilities, and social infrastructure escalates. Governments and private sectors are heavily investing in smart city initiatives, which require extensive civil engineering expertise to ensure that infrastructure is both efficient and sustainable. Furthermore, the expansion of megacities in emerging economies is creating a significant need for advanced civil engineering services, ranging from planning and design to construction and maintenance.



    Another significant growth driver is the increasing focus on sustainable and resilient infrastructure. The threat of climate change has led to an emphasis on building structures that can withstand extreme weather conditions and natural disasters. This involves incorporating green building materials, energy-efficient designs, and disaster-resistant technologies into construction projects. Governments and regulatory bodies are also implementing stringent building codes and standards, which necessitate the involvement of skilled civil engineers to ensure compliance. As a result, the demand for specialized civil engineering services is on the rise.



    Technological advancements are also playing a crucial role in the growth of the civil engineering market. The adoption of Building Information Modeling (BIM), Geographic Information Systems (GIS), and other advanced software tools has revolutionized the way civil engineering projects are planned and executed. These technologies improve precision, reduce errors, and enhance collaboration among stakeholders. Additionally, innovations in materials science, such as the development of high-performance concrete and smart materials, are contributing to the creation of more durable and efficient infrastructures. These technological strides are attracting significant investment and interest in the civil engineering sector.



    Regionally, the Asia-Pacific area is expected to dominate the civil engineering market due to rapid economic growth and substantial infrastructure development in countries like China and India. North America and Europe are also significant markets, driven by the need to upgrade aging infrastructure and implement smart city projects. The Middle East & Africa and Latin America regions present considerable growth opportunities due to ongoing urbanization and investment in infrastructure projects. Each region has its unique drivers and challenges, but the overall outlook for the civil engineering market remains robust.



    Service Type Analysis


    Planning & Design



    The planning and design segment is a critical component of the civil engineering market. This segment involves the initial stages of any construction project, where feasibility studies, site surveys, and detailed project plans are developed. The rising complexity of modern infrastructure projects necessitates meticulous planning and innovative design solutions. Advanced software tools such as AutoCAD, Revit, and BIM are extensively utilized in this segment to create accurate and efficient designs. The integration of these tools helps streamline the planning process, reduce errors, and ensure that the final design meets all regulatory and safety standards.



    Sustainable design practices are gaining prominence within the planning and design segment. With increasing awareness of environmental issues, there is a growing emphasis on creating eco-friendly and energy-efficient building designs. This involves the use of green building materials, renewable energy sources, and waste reduction strategies. Civil engineers are now focusing on designing structures that minimize environmental impact while maximizing functionality and aesthetics. This shift towards sustainability is driving innovation and growth in the planning and design segment.


    <br /&

  17. C

    Birmingham Civil Rights National Monument Boundary

    • data.birminghamal.gov
    geojson, html, shp
    Updated Apr 18, 2018
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    Birmingham Planning & Engineering (2018). Birmingham Civil Rights National Monument Boundary [Dataset]. https://data.birminghamal.gov/dataset/birmingham-civil-rights-national-monument-boundary
    Explore at:
    html, geojson, shpAvailable download formats
    Dataset updated
    Apr 18, 2018
    Dataset authored and provided by
    Birmingham Planning & Engineering
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Birmingham
    Description

    Planning, Engineering & Permitting - Birmingham Civil Rights National Monument Boundary

  18. Z

    Supplementary GIS data - Potential and implications of automated...

    • data.niaid.nih.gov
    • zenodo.org
    Updated Jun 28, 2024
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    David Novák; Filip Pružinec (2024). Supplementary GIS data - Potential and implications of automated pre-processing of LiDAR-based digital elevation models for large-scale archaeological landscape analysis [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_6368000
    Explore at:
    Dataset updated
    Jun 28, 2024
    Dataset provided by
    Czech Academy of Sciences, Institute of Archaeology, Prague
    Faculty of Civil Engineering, Slovak University of Technology in Bratislava
    Authors
    David Novák; Filip Pružinec
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    A supplementary dataset related to the paper discussing preparation of a digital elevation model derived from DMR 5G (LiDAR-based DEM of the Czech Republic) cleaned of modern artificial features. It includes data used as a clipping mask and data produced during the testing phase.

    Contents:

    ..\clipping_buffers.gdb\ - Clipping buffers based on ZABAGED dataset used for masking the original data stored as ESRI geodatabase.

    ..\drainages\ - Drainages with Strahler order higher than four (potential watercourses) for the original and filtered DEMs.

    drainages_filtered - Drainges identified in the filtered DEM stored as GeoTIFF.

    drainages_original - Drainges identified in the original DEM stored as GeoTIFF.

    ..\LSC\ - Locations with significant land surface curvature for the original and filtered DEMs.

    LSC_filtered - Significant LSC identified in the filtered DEM stored as GeoTIFF.

    LSC_original - Significant LSC identified in the original DEM stored as GeoTIFF.

    ..\visibility\ - Viewsheds computed over the original and filtered DEMs.

    Libice\ - Sample viewsheds computed for the early medieval hillfort of Libice.

    Libice_visibility_filtered - Viewshed based on the filtered DEM stored as GeoTIFF.

    Libice_visibility_original - Viewshed based on the original DEM stored as GeoTIFF.

    observer_points - Observer points used for calculating the viewsheds.

    regular_grid\ - Cumulative viewsheds calculated for regularly spaced points in a 10 x 10 km grid with a visibility radius of 5 km and an observer height of 2 m; a total of 574 viewsheds.

    visibility_filtered - Cumulative viewshed for the filtered DEM stored as GeoTIFF.

    visibility_original - Cumulative viewshed for the original DEM stored as GeoTIFF.

    visibility_test_buffers - Buffers used for the viewshed calculations stored as ESRI shapefile.

    visibility_test_observers - Observer points used for the viewshed calculations stored as ESRI shapefile.

    Preprint version of the related paper:

    Novák, David and Pružinec, Filip, Potential and Implications of Automated Pre-Processing of Lidar-Based Digital Elevation Models for Large-Scale Archaeological Landscape Analysis. Available at SSRN: https://ssrn.com/abstract=4063514

  19. f

    Research data - Shelf-wide analyses of sand wave migration using GIS. A case...

    • figshare.com
    zip
    Updated Jun 4, 2023
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    Rens van der Meijden; Johan Damveld; David Ecclestone; J. J. (Jebbe) van der Werf; Pieter C. Roos (2023). Research data - Shelf-wide analyses of sand wave migration using GIS. A case study on the Netherlands Continental Shelf [Dataset]. http://doi.org/10.4121/21428466.v1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 4, 2023
    Dataset provided by
    4TU.ResearchData
    Authors
    Rens van der Meijden; Johan Damveld; David Ecclestone; J. J. (Jebbe) van der Werf; Pieter C. Roos
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Area covered
    Netherlands
    Description

    We analysed the spatial variability of tidal sand wave migration for all sand wave fields on the Netherlands Continental Shelf. The migration data obtained within this research is available via this repository. For further instructions see the README files contained within the compressed .zip folder.

  20. m

    Data in multi-hazard risk assessment using MCDM methods combined with GIS in...

    • data.mendeley.com
    Updated Jan 30, 2023
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    Haimin Lyu (2023). Data in multi-hazard risk assessment using MCDM methods combined with GIS in Hong Kong [Dataset]. http://doi.org/10.17632/3pwj4bjfcb.1
    Explore at:
    Dataset updated
    Jan 30, 2023
    Authors
    Haimin Lyu
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Hong Kong
    Description

    The data were used to introduce the judgment matrixes, which can be used to calculate the weight coefficients of AHP, interval FAHP and ANP methods for the multi-hazard risk assessment using multi-criteria decision making (MCDM).

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Einetic (2023). GIS [Dataset]. https://paper.erudition.co.in/makaut/btech-in-civil-engineering/8/gis-and-remote-sensing

GIS

5

Explore at:
htmlAvailable download formats
Dataset updated
May 11, 2023
Dataset authored and provided by
Einetic
License

https://paper.erudition.co.in/termshttps://paper.erudition.co.in/terms

Description

Question Paper Solutions of chapter GIS of GIS & Remote Sensing, 8th Semester , Civil Engineering

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