Geographic information systems in urban planning
Geographic information systems in urban planning
Dr. Tamaro Green, DS
With growing trends in urbanization, an application of geographic information systems is visualizing geographic and geological data with machine learning and big data. Big data and machine learning may support models, simulations, and visualizations of geographic and geological data (Stum, Buttenfield, & Stanislawski, 2017). Stum et al. (2017) explain the process of developing geometric shapes to represent the geological features on maps. Stanley and Kirschbaum (2017) describe how heuristic fuzzy models can be integrated with geographic information system software for the development of machine learning models. Sorokine, Karthik, King, and Budhendra (2016) describe how big data and high performance computing can support research on urbanization by allowing visualization of urban data.
Urban planning may require comprehension of how demographic groups are affected by displacement. Data mining tools may be integrated with geographic information systems to provide visualization of data. Gokalp, Kayabay, Akyol, Eren, and Koçyiğit (2016) explain Orange, a data mining tool that can create charts and graphs. Data visualization tools can read data from various sources and display data in a number of formats. Data visualization tools can also integrate with GIS applications to display data based on location with interactive maps (Szewrański, Kazak, Sylla, & Świąder, 2017). Data visualization tools can also be integrated with data sources to provide real time analytics.
Emerging data formats also may have the potential to support urban planning with geographic information systems. Ogden, Thomas, and Pietzuch (2016) explain that common formats for spatial data include GeoJSON and XML. Evans et al. (2019) describe techniques that have developed for solving these computational problems have also included round-robin data partition and key-value store. Evans et al. (2019) explains that the development of spatial big data has created new challenges in comparison to what is becoming traditional big data. Evans et al. (2019) explains solutions for spatial big data include Spatial Hadoop, GIS on Hadoop, and declustering.
Big data analysis tools may enable evaluation of large maps or datasets. Daki, Asmaa El, Aqqal, Haidine, and Aziz (2017) highlight advantages that big data can have in geographic information systems for maps, spatial data, and points of interest. Arya, Sharma, Singh, and Silva (2017) explain that visualizations create the ability to naturally identify patterns and anomalies in data. Hirzel, Schneider, and Ged˙Ik (2017) provide an example of visualizations for analyzing streams of data for network vulnerabilities. The R gis library allows for the display of map data in R. A number of maps are available for access through the library. Additional map data can also be added as shape files. Points and lines can be added to maps via the latitude and longitude points. The maps can provide realistic representations of local data in R.
Applications of big data analysis may demonstrate strategies for urban planning. Cushing, Winters, and Lach (2015) explain the Visualization for Terrestrial and Aquatic Systems project, VISTAS, which has been implemented as an open-source GIS modeling tool for scenario-based planning and environmental assessments with WebGL, a JavaScript API, and digital elevation maps. Cushing et al. (2015) also describe another implementation of VISTAS to create ecological and hydrological models. Cushing et al. (2015) describe the knowledge to action network method as a technique for producing and evaluating knowledge.
Data mining provides resources for management information systems in urban planning. Data mining can be characterized as knowledge discovery in database systems (Silberschatz, Korth, & Sudarshan, 1997). Rules are a common framework for data mining. Data mining rules can be grouped into two forms, classification and association rules. A classification tree can represent the outcome of a training set. Two types of spatial databases include CAD and GIS databases (Cros et al., 2014). Software Test News will continue to provide research on applications of geographic information systems in urban planning.
References:
Cros, A., Ahamad Fatan, N., White, A., Teoh, S. J., Tan, S., Handayani, C., . . . Beare, D. (2014). The Coral Triangle Atlas: An Integrated Online Spatial Database System for Improving Coral Reef Management. PLoS ONE, 9(6), 1–7. doi:10.1371/journal.pone.0096332
Cushing, J. B., Winters, K. M., & Lach, D. (2015). Software for scientists facing wicked problems lessons from the VISTAS project. Paper presented at the Proceedings of the 16th Annual International Conference on Digital Government Research, Phoenix, Arizona.
Evans, M. R., Oliver, D., Yang, K., Zhou, X., Ali, R. Y., & Shekhar, S. (2019). Enabling spatial big data via CyberGIS: Challenges and opportunities. In CyberGIS for Geospatial Discovery and Innovation (pp. 143–170): Springer.
Gokalp, M. O., Kayabay, K., Akyol, M. A., Eren, P. E., & Koçyiğit, A. (2016, 15–17 Dec. 2016). Big data for Industry 4.0: A conceptual framework. Paper presented at the 2016 International Conference on Computational Science and Computational Intelligence (CSCI).
Ogden, P., Thomas, D., & Pietzuch, P. (2016). AT-GIS: Highly parallel spatial query processing with associative transducers. Paper presented at the Proceedings of the 2016 International Conference on Management of Data, San Francisco, California, USA.
Silberschatz, A., Korth, H. F., & Sudarshan, S. (1997). Database system concepts (Vol. 4): McGraw-Hill New York.
Sorokine, A., Karthik, R., King, A., & Budhendra, B. (2016). Big data as a service from an urban information system. Paper presented at the Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data — BigSpatial ‘16.
Stanley, T., & Kirschbaum, D. B. (2017). A heuristic approach to global landslide susceptibility mapping. Natural Hazards, 87(1), 145–164. doi:10.1007/s11069–017–2757-y
Stum, A. K., Buttenfield, B. P., & Stanislawski, L. V. (2017). Partial polygon pruning of hydrographic features in automated generalization. Transactions in GIS, 21(5), 1061–1078. doi:10.1111/tgis.12270
Szewrański, S., Kazak, J., Sylla, M., & Świąder, M. (2017). Spatial data analysis with the use of ArcGIS and Tableau Systems. In The Rise of Big Spatial Data (pp. 337–349): Springer.
