AN OVERVIEW OF AI AND GEOSPATIAL DATA TOWARDS IMPROVED STRATEGIC DECISIONS AND AUTOMATED BUSINESS DECISION PROCESS
DOI:
https://doi.org/10.56177/11icmie2023.30Keywords:
Artificial Intelligence, Geospatial data, Decision making, GeoAI: Strategic decisions, Geographic Information System (GIS).Abstract
In recent years, the availability and accessibility of geospatial data have greatly increased, providing valuable information for various industries such as transportation, real estate, and retail. However, the great volume and complexity of this data can make it difficult for businesses to extract meaningful insights. By incorporating AI algorithms such as machine learning or deep learning techniques, businesses can more effectively manage, analyze and interpret the data. This paper provides an overview of the current challenges and opportunities in using geospatial data for business decision-making and explores how artificial intelligence (AI) techniques can be applied to better in-form strategic decisions and improve operational efficiency. The study aims to identify best practices and design principles for creating effective automated decision-making systems using AI and geospatial data. A systematic review of the literature was conducted to address the research questions, which revealed that despite challenges, geospatial data presents numerous opportunities for business decision-making. The results suggest that integrating AI techniques can enhance the efficiency and accuracy of geospatial data analysis, enabling organizations to make more informed strategic decisions. This study highlights the importance of adopting best practices and design principles to maximize the benefits of geospatial data and AI for business decision-making. Overall, this paper argues that the integration of AI and geospatial data analysis has the potential to revolutionize the way businesses make decisions and gain insights from their data
References
K. Janowicz, S. Gao, G. McKenzie, Y. Hu, and B. Bhaduri, “GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond,” International Journal of Geographical Information Science, vol. 34, no. 4, pp. 625–636, Apr. 2020, doi: 10.1080/13658816.2019.1684500.
C. Yang, M. Yu, F. Hu, Y. Jiang, and Y. Li, “Utilizing Cloud Computing to address big geospatial data challenges,” Computers, Environment and Urban Systems, vol. 61, pp. 120–128, Jan. 2017, doi: 10.1016/j.compenvurbsys.2016.10.010.
J.-G. Lee and M. Kang, “Geospatial Big Data: Challenges and Opportunities,” Big Data Research, vol. 2, no. 2, pp. 74–81, Jun. 2015, doi: 10.1016/j.bdr.2015.01.003.
K.-F. Richter and S. Scheider, “Current topics and challenges in geoAI,” Künstl Intell, Jan. 2023, doi: 10.1007/s13218-022-00796-0.
D. B. Richardson, M.-P. Kwan, G. Alter, and J. E. McKendry, “Replication of scientific research: addressing geoprivacy, confidentiality, and data sharing challenges in geospatial research,” Annals of GIS, vol. 21, no. 2, pp. 101–110, Apr. 2015, doi: 10.1080/19475683.2015.1027792.
O. Rahman, B. C. M. Fung, and W. Liu, “Using data mining to analyse fashion consumers’ preferences from a cross-national perspective,” International Journal of Fashion Design, Technology and Education, vol. 7, no. 1, pp. 42–49, Jan. 2014, doi: 10.1080/17543266.2013.864340.
S. K. Jauhar, S. M. Jani, S. S. Kamble, S. Pratap, A. Belhadi, and S. Gupta, “How to use no-code artificial intelligence to predict and minimize the inventory distortions for resilient supply chains,” International Journal of Production Research, pp. 1–25, Jan. 2023, doi: 10.1080/00207543.2023.2166139.
S. Maheshwari, P. Gautam, and C. K. Jaggi, “Role of Big Data Analytics in supply chain management: current trends and future perspectives,” International Journal of Production Research, vol. 59, no. 6, pp. 1875–1900, Mar. 2021, doi: 10.1080/00207543.2020.1793011.
M. von Stietencron et al., “Towards logistics 4.0: an edge-cloud software framework for big data analytics in logistics processes,” International Journal of Production Research, vol. 60, no. 19, pp. 5994–6012, Oct. 2022, doi: 10.1080/00207543.2021.1977408.
J.-H. Hong and M.-L. Huang, “Enabling smart data selection based on data completeness measures: a quality-aware approach,” International Journal of Geographical Information Science, pp. 1–20, Feb. 2017, doi: 10.1080/13658816.2017.1290251.
J. A. Gómez, J. E. Patiño, J. C. Duque, and S. Passos, “Spatiotemporal Modeling of Urban Growth Using Machine Learning,” Remote Sensing, vol. 12, no. 1, p. 109, Dec. 2019, doi: 10.3390/rs12010109.
J. Dold and J. Groopman, “The future of geospatial intelligence,” Geo-spatial Information Science, vol. 20, no. 2, pp. 151–162, Apr. 2017, doi: 10.1080/10095020.2017.1337318.
M. N. Kamel Boulos, G. Peng, and T. VoPham, “An overview of GeoAI applications in health and healthcare,” Int J Health Geogr, vol. 18, no. 1, pp. 7, s12942-019-0171–2, Dec. 2019, doi: 10.1186/s12942-019-0171-2.
M. J. Kraak and F. Ormeling, Cartography: visualization of geospatial data, 3rd ed. Harlow ; New York: Prentice Hall, 2010.
R. Pierdicca and M. Paolanti, “GeoAI: a review of Artificial Intelligence approaches for the interpretation of complex Geomatics data,” Data management, preprint, Jan. 2022. doi: 10.5194/gi-2021-32.
Z. Sun, L. Di, A. Burgess, J. A. Tullis, and A. B. Magill, “Geoweaver: Advanced Cyberinfrastructure for Managing Hybrid Geoscientific AI Workflows,” IJGI, vol. 9, no. 2, p. 119, Feb. 2020, doi: 10.3390/ijgi9020119.
R. Wen and S. Li, “Spatial Decision Support Systems with Automated Machine Learning: A Review,” IJGI, vol. 12, no. 1, p. 12, Dec. 2022, doi: 10.3390/ijgi12010012.
P. B. Keenan and P. Jankowski, “Spatial Decision Support Systems: Three decades on,” Decision Support Systems, vol. 116, pp. 64–76, Jan. 2019, doi: 10.1016/j.dss.2018.10.010.
A. Kaginalkar, S. Kumar, P. Gargava, and D. Niyogi, “Stakeholder analysis for designing an urban air quality data governance ecosystem in smart cities,” Urban Climate, vol. 48, p. 101403, Mar. 2023, doi: 10.1016/j.uclim.2022.101403.
M. Pejic Bach and N. Cerpa, “Editorial: Planning, Conducting and Communicating Systematic Literature Reviews,” J. theor. appl. electron. commer. res., vol. 14, no. 3, pp. 190–192, Sep. 2019, doi: 10.4067/S0718-18762019000300101.
L. E. Ferro-Diez, N. M. Villegas, and J. Diaz-Cely, “Location Data Analytics in the Business Value Chain: A Systematic Literature Review,” IEEE Access, vol. 8, pp. 204639–204659, 2020, doi: 10.1109/ACCESS.2020.3036835.
C. Willing, K. Klemmer, T. Brandt, and D. Neumann, “Moving in time and space – Location intelligence for carsharing decision support,” Decision Support Systems, vol. 99, pp. 75–85, Jul. 2017, doi: 10.1016/j.dss.2017.05.005.
L. Masiero, Y. Yang, and R. T. R. Qiu, “Understanding hotel location preference of customers: Comparing random utility and random regret decision rules,” Tourism Management, vol. 73, pp. 83–93, Aug. 2019, doi: 10.1016/j.tourman.2018.12.002.
L. Kowalski, “Comparing Spatial-Interaction and Hybrid Agent-Based Modelling Approaches: An Application to Location Analysis of Services,” JASSS, vol. 22, no. 1, p. 1, 2019, doi: 10.18564/jasss.3899.
S. J. Miah, H. Q. Vu, and J. G. Gammack, “A Location Analytics Method for the Utilisation of Geotagged Photos in Travel Marketing Decision-Making,” J. Info. Know. Mgmt., vol. 18, no. 01, p. 1950004, Mar. 2019, doi: 10.1142/S0219649219500047.
B. Nikparvar and J.-C. Thill, “Machine Learning of Spatial Data,” IJGI, vol. 10, no. 9, p. 600, Sep. 2021, doi: 10.3390/ijgi10090600.
E. Osuteye, C. Johnson, and D. Brown, “The data gap: An analysis of data availability on disaster losses in sub-Saharan African cities,” International Journal of Disaster Risk Reduction, vol. 26, pp. 24–33, Dec. 2017, doi: 10.1016/j.ijdrr.2017.09.026.
J. Xing and R. Sieber, “Integrating XAI and GeoAI,” GIScience 2021 Short Paper Proceedings. 11th International Conference on Geographic Information Science. September 27-30, vol. 2021. Poznań, p. Poland (Online)., 2021, doi: 10.25436/E23014.
J. Wang, J. Kim, and M.-P. Kwan, “An exploratory assessment of the effectiveness of geomasking methods on privacy protection and analytical accuracy for individual-level geospatial data,” Cartography and Geographic Information Science, vol. 49, no. 5, pp. 385–406, Sep. 2022, doi: 10.1080/15230406.2022.2056510.
M. del R. Martínez-Torres, F. Rodriguez-Piñero, and S. L. Toral, “Customer preferences versus managerial decision-making in open innovation communities: the case of Starbucks,” Technology Analysis & Strategic Management, vol. 27, no. 10, pp. 1226–1238, Nov. 2015, doi: 10.1080/09537325.2015.1061121.
R. Bai et al., “Analytics and machine learning in vehicle routing research,” International Journal of Production Research, vol. 61, no. 1, pp. 4–30, Jan. 2023, doi: 10.1080/00207543.2021.2013566.
S. Shams Amiri, M. Mueller, and S. Hoque, “Investigating the application of a commercial and residential energy consumption prediction model for urban Planning scenarios with Machine Learning and Shapley Additive explanation methods,” Energy and Buildings, vol. 287, p. 112965, May 2023, doi: 10.1016/j.enbuild.2023.112965.
B. Zhang, M. Zhao, and X. Hu, “Location planning of electric vehicle charging station with users’ preferences and waiting time: multi-objective bi-level programming model and HNSGA-II algorithm,” International Journal of Production Research, vol. 61, no. 5, pp. 1394–1423, Mar. 2023, doi: 10.1080/00207543.2021.2023832.
P. Tsagkis, E. Bakogiannis, and A. Nikitas, “Analysing urban growth using machine learning and open data: An artificial neural network modelled case study of five Greek cities,” Sustainable Cities and Society, vol. 89, p. 104337, Feb. 2023, doi: 10.1016/j.scs.2022.104337.
R. Al-Naim and Y. Lytkin, “Review and comparison of prediction algorithms for the estimated time of arrival using geospatial transportation data,” Procedia Computer Science, vol. 193, pp. 13–21, 2021, doi: 10.1016/j.procs.2021.11.003.
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