Abstract
By viewing the existing situation of rapid urbanization around the globe, it is very clear that more than 90% of the earth's population will be settled in cities till the end of this century. Though big cities are multifaceted and dynamic by nature, but various problems are attached with it due to high densities and bulk of activities, including urban heating (UH), environmental pollution, crimes, expensive accommodation, traffic congestion etc. However, due to the advances in digital technology and internet services social media is exposing all types of information linked to our daily lives and these networks produce a vibrant situation which are exhilarating. To deal with all these dynamics positively, it is of immense importance to generate a geo-database of cities. The urban areas having geographic database and strong sensor networks to observe the city dynamics are marked as smart cities. The Geographic Information system have a vital role in understanding all of the city dynamics and provides appropriate data to support the decision-making process. Though the big data geo processing received from sensor networks is still challenging. The urban areas having geographic database and strong sensor networks to observe the city dynamics are marked as smart cities. The Urban Informatics (UI) provides a better view of city dynamics by using the Internet of Things (IoT) and big data. It could add much in data-driven decisions. Though, the UI is a comparatively new field which is working with various data types to understand the city dynamics in a better way by using iterative analysis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Desa UN (2019) World population prospects 2019: highlights. New York(US): UN Dept Econ Soc Affairs 11(1):125. https://population.un.org/wpp/publications/files/wpp2019_highlights.pdf
United nations-world urbanization prospects (2015) The 2014 revision—highlights http://esa.un.org/unpd/wup/Highlights/WUP2014-Highlights.pdf. https://population.un.org/wup/publications/files/wup2014-report.pdf
Madanian M, Soffianian AR, Koupai SS, Pourmanafi S, Momeni M (2018) Analyzing the effects of urban expansion on land surface temperature patterns by landscape metrics: a case study of Isfahan city, Iran. Environ Monit Assess 190:1–11. https://pubmed.ncbi.nlm.nih.gov/29502210/
Kuddus MA, Tynan E, McBryde E (2020) Urbanization: a problem for the rich and the poor? Public Health Rev 41(1):1–4. https://doi.org/10.1186/s40985-019-0116-0
Gul S, Bibi T, Rahim S, Gul Y, Niaz A, Mumtaz S, Shedayi AA (2022) Spatio-temporal change detection of land use and land cover in Malakand Division Khyber Pakhtunkhwa, Pakistan, using remote sensing and geographic information system. Environ Sci Pollut Res 1–13. https://doi.org/10.1007/s11356-022-22960-7
Piracha A, Chaudhary MT (2022) Urban air pollution, urban heat island and human health: a review of the literature. Sustainability 14(15):9234. https://doi.org/10.3390/su14159234
Wang Y, Lehnert LW, Holzapfel M, Schultz R, Heberling G, Görzen E, Meyer H, Seeber E, Pinkert S, Ritz M, Fu Y (2018) Multiple indicators yield diverging results on grazing degradation and climate controls across Tibetan pastures. Ecol Ind 93:1199–1208. https://doi.org/10.1016/j.ecolind.2018.06.021
Bibi T, Gul Y, Abdul Rahman A, Riaz M (2016) Landslide susceptibility assessment through fuzzy logic inference system (FLIS). Int Arch Photogram Rem Sens Spat Inf Sci 42. https://doi.org/10.5194/isprs-archives-XLII-4-W1-355-2016
Bibi T, Nawaz F, Abdul Rahman A, Azahari Razak K, Latif A (2018) Flood risk assessment of river Kabul and swat catchment area: district Charsadda, Pakistan. The international archives of the photogrammetry. Rem Sens Spat Inf Sci 42(4/W9). https://doi.org/10.5194/isprs-archives-XLII-4-W9-105-2018
Rosell C, Seiler A, Chretien LTS, Guinard E, Trocmé M, Hlavac V, van der Grift EA (2022) COST 341 habitat fragmentation due to transportation infrastructure: wildlife and traffic a european handbook for identifying conflicts and designing solutions. https://handbookwildlifetraffic.info
Kantakumar LN, Neelamsetti P (2015) Multi-temporal land use classification using hybrid approach. Egypt J Rem Sens Space Sci 18(2):289–295. https://doi.org/10.1016/j.ejrs.2015.09.003
Zhong SH, Liu Y, Ren F, Zhang J, Ren T (2013) Video saliency detection via dynamic consistent spatio-temporal attention modelling. In: Twenty-seventh AAAI conference on artificial intelligence. https://doi.org/10.1609/aaai.v27i1.8642
Sabri S, Witte P (2023) Digital technologies in urban planning and urban management. J Urban Manage 12(1):1–3. https://doi.org/10.1016/j.jum.2023.02.003
Pan H, Geertman S, Deal B (2020) What does urban informatics add to planning support technology? Environ Plan B Urban Anal City Sci 47(8):1317–1325. https://doi.org/10.1177/2399808320945453
Kontokosta C, Lai Y, Bonzak B, Papadopoulos S, Hong B, Johnson N, Malik A (2018) A dynamic spatial-temporal model of urban carbon emissions for data-driven climate action by cities. Bloomberg Data for Good Exchange 2018. https://data.bloomberglp.com/company/sites/2/2018/09/Big-Data-for-Climate-Action.pdf
Hearn G, Foth M (2007). Communicative ecologies. Electron J Commun 17(1–2). Communication Institute for Online Scholarship, New York. https://eprints.qut.edu.au/8171/
Foth M, Choi JH, Bilandzic M, Satchell C (2008). Collective and network sociality in an urban village. In: Proceedings of the 12th international conference on Entertainment and media in the ubiquitous era, pp 179–183. https://doi.org/10.1145/1457199.1457239
Leadbeater C (2010) We-think: mass innovation, not mass production. Profile books. https://www.google.com.pk/books/edition/We_Think/f9ql4ZX0uKAC?hl=en&gbpv=0
Sahar G, Bakar KB, Zuhra FT, Rahim S, Bibi T, Madni SH (2021) Data redundancy reduction for energy-efficiency in wireless sensor networks: a comprehensive review. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3128353
Sahar G, Bakar KA, Rahim S, Khani NA, Bibi T (2021) Recent advancement of data-driven models in wireless sensor networks: a survey. Technologies 9(4):76. https://doi.org/10.3390/technologies9040076
Bibi T, Azahari K, Abdul Rahman A, Latif A (2017) Spatio temporal detection and virtual mapping of landslide using high-resolution airborne laser altimetry (LIDAR) in densely vegetated areas of tropics. Int Arch Photogram Rem Sens Spat Inf Sci 42. https://doi.org/10.5194/isprs-archives-XLII-4-W5-21-2017
Sanchez TW, Shumway H, Gordner T, Lim T (2023) The prospects of artificial intelligence in urban planning. Int J Urban Sci 27(2):179–94. https://doi.org/10.1080/12265934.2022.2102538
Avdić A, Marovac U, Janković D (2022) Smart health services based on IoT and GIS. In: Camacho D, Rosaci D, Sarné GML, Versaci M (eds) Intelligent distributed computing XIV. IDC 2021. Studies in computational intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_2
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Bibi, T., Latif, A., Irum, S., Ashfaq, M. (2024). Geo-Smart City Applications. In: Yadava, R.N., Ujang, M.U. (eds) Advances in Geoinformatics Technologies . Earth and Environmental Sciences Library. Springer, Cham. https://doi.org/10.1007/978-3-031-50848-6_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-50848-6_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-50847-9
Online ISBN: 978-3-031-50848-6
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)