Geoint as a Driver of National Security

Authors

  • Riefda Novikarany Defense University

DOI:

https://doi.org/10.55927/ijsmr.v3i2.66

Keywords:

National Security, Geospatial, GEOINT, Intelligence, Threat

Abstract

Geospatial Intelligence (GEOINT) is a pivotal driver of national security, integrating advanced analytical techniques to strengthen law enforcement, analyze movement patterns, and enhance geospatial data accuracy. This study highlights the role of GEOINT in addressing critical security challenges such as terrorism, threat analysis, and decision-making through intelligence surveillance. As an essential component of GEOINT, movement pattern analysis underpins its implementation and contributions across various countries, showcasing its adaptability to diverse security frameworks. Furthermore, techniques such as Geographic Information Systems (GIS), Artificial Intelligence (AI), and the analysis of relative motion augment the effectiveness of geospatial analysis by increasing the probability of accurate surveillance and data interpretation. The study underscores GEOINT's pivotal role in validating criminal data and optimizing workflow for law enforcement agencies, emphasizing its importance as a multidisciplinary tool in modern security operations. These findings demonstrate GEOINT's transformative impact on ensuring global safety and fortifying national security strategies

References

12-Duncan.pdf. (n.d.).

Alastal, A. I., & Shaqfa, A. H. (2022). GeoAI Technologies and Their Application Areas in Urban Planning and Development: Concepts, Opportunities and Challenges in Smart City (Kuwait, Study Case). Journal of Data Analysis and Information Processing, 10(02), 110–126. https://doi.org/10.4236/jdaip.2022.102007

Alexopoulos, T. A. (2023). On global maritime oil piracy: an association rules analysis. Energy Systems, 0123456789. https://doi.org/10.1007/s12667-023-00639-3

Barb, A. S., & Shyu, C. R. (2010). Visual-semantic modeling in content-based geospatial information retrieval using associative mining techniques. IEEE Geoscience and Remote Sensing Letters, 7(1), 38–42. https://doi.org/10.1109/LGRS.2009.2017214

Benà, E., Ciotoli, G., Petermann, E., Bossew, P., Ruggiero, L., Verdi, L., Huber, P., Mori, F., Mazzoli, C., & Sassi, R. (2024). A new perspective in radon risk assessment: Mapping the geological hazard as a first step to define the collective radon risk exposure. Science of the Total Environment, 912(September 2023). https://doi.org/10.1016/j.scitotenv.2023.169569

Bengtsson, L., Lu, X., Thorson, A., Garfield, R., & von Schreeb, J. (2011). Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: A post-earthquake geospatial study in haiti. PLoS Medicine, 8(8), 1–9. https://doi.org/10.1371/journal.pmed.1001083

Borges, D. E., Ramage, S., Green, D., Justice, C., Nakalembe, C., Whitcraft, A., Barker, B., Becker-Reshef, I., Balagizi, C., Salvi, S., Ambrosia, V., San-Miguel-Ayanz, J., Boschetti, L., Field, R., Giglio, L., Kuhle, L., Low, F., Kettner, A., Schumann, G., … Reichenbach, K. (2023). Earth observations into action: the systemic integration of earth observation applications into national risk reduction decision structures. Disaster Prevention and Management: An International Journal, 32(1), 163–185. https://doi.org/10.1108/DPM-09-2022-0186

Brennan, S., Coulthart, S., & Nussbaum, B. (2023). The Brave New World of Third Party Location Data. Journal of Strategic Security, 16(2), 81–95. https://doi.org/10.5038/1944-0472.16.2.2070

Bunch, A. W., Kim, M., & Brunelli, R. (2017). Under Our Nose: The Use of GIS Technology and Case Notes to Focus Search Efforts. Journal of Forensic Sciences, 62(1), 92–98. https://doi.org/10.1111/1556-4029.13218

Butkovic, A., Orucevic, F., & Tanovic, A. (2013). Using Whois Based Geolocation and Google Maps API for support cybercrime investigations. Recent Advances in Telecommunications and Circuits Using, June 2013, 194–200.

Changmai, S., Saran, S., & Gupta, P. K. (2023). Geospatial Application for Dairy Supply Chain Management. Journal of Geomatics, 17(2), 174–183. https://doi.org/10.58825/jog.2023.17.2.63

Cowen, C., Louderback, E. R., & Roy, S. Sen. (2019). The role of land use and walkability in predicting crime patterns: A spatiotemporal analysis of Miami-Dade County neighborhoods, 2007–2015. Security Journal, 32(3), 264–286. https://doi.org/10.1057/s41284-018-00161-7

Daly, C., Gibson, W. P., Taylor, G. H., Johnson, G. L., & Pasteris, P. (2002). A knowledge-based approach to the statistical mapping of climate. Climate Research, 22(2), 99–113. https://doi.org/10.3354/cr022099

Dold, J., & Groopman, J. (2017). The future of geospatial intelligence. Geo-Spatial Information Science, 20(2), 151–162. https://doi.org/10.1080/10095020.2017.1337318

Döllner, J. (2020). Geospatial Artificial Intelligence: Potentials of Machine Learning for 3D Point Clouds and Geospatial Digital Twins. PFG - Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 88(1), 15–24. https://doi.org/10.1007/s41064-020-00102-3

Feng, M., Shaw, S. L., Fang, Z., & Cheng, H. (2019). Relative space-based GIS data model to analyze the group dynamics of moving objects. ISPRS Journal of Photogrammetry and Remote Sensing, 153(November 2018), 74–95. https://doi.org/10.1016/j.isprsjprs.2019.05.002

Goel, R. K., Yadav, C. S., Vishnoi, S., & Rastogi, R. (2021). Smart agriculture – Urgent need of the day in developing countries. Sustainable Computing: Informatics and Systems, 30(December 2019), 100512. https://doi.org/10.1016/j.suscom.2021.100512

González-González, A., Clerici, N., & Quesada, B. (2021). Growing mining contribution to Colombian deforestation. Environmental Research Letters, 16(6). https://doi.org/10.1088/1748-9326/abfcf8

Hart, T. C., & Zandbergen, P. A. (2013). Reference data and geocoding quality: Examining completeness and positional accuracy of street geocoded crime incidents. In Policing (Vol. 36, Issue 2). https://doi.org/10.1108/13639511311329705

Hegazy, I. R., & Kaloop, M. R. (2015). Monitoring urban growth and land use change detection with GIS and remote sensing techniques in Daqahlia governorate Egypt. International Journal of Sustainable Built Environment, 4(1), 117–124. https://doi.org/10.1016/j.ijsbe.2015.02.005

Hong Diep, N. T., Nguyen, C. T., Diem, P. K., Hoang, N. X., & Kafy, A. Al. (2022). Assessment on controlling factors of urbanization possibility in a newly developing city of the Vietnamese Mekong delta using logistic regression analysis. Physics and Chemistry of the Earth, 126(March 2021), 103065. https://doi.org/10.1016/j.pce.2021.103065

Jimenez Velez, A. F. (2023). Geospatial Collective Intelligence Approach in the appreciation phase of military planning. Ciencia y Poder Aéreo, 18(2), 67–74. https://doi.org/10.18667/cienciaypoderaereo.772

Jones, A., Koehler, S., Jerge, M., Graves, M., King, B., Dalrymple, R., Freese, C., & Von Albade, J. (2023). BATMAN: A Brain-like Approach for Tracking Maritime Activity and Nuance. Sensors, 23(5). https://doi.org/10.3390/s23052424

Jones, A., Kuehnert, J., Fraccaro, P., Meuriot, O., Ishikawa, T., Edwards, B., Stoyanov, N., Remy, S. L., Weldemariam, K., & Assefa, S. (2023). AI for climate impacts: applications in flood risk. Npj Climate and Atmospheric Science, 6(1). https://doi.org/10.1038/s41612-023-00388-1

Li, J., Hong, D., Gao, L., Yao, J., Zheng, K., Zhang, B., & Chanussot, J. (2022). Deep learning in multimodal remote sensing data fusion: A comprehensive review. International Journal of Applied Earth Observation and Geoinformation, 112(April), 102926. https://doi.org/10.1016/j.jag.2022.102926

Li, L., Wang, J., & Wang, C. (2005). Typhoon insurance pricing with spatial decision support tools. International Journal of Geographical Information Science, 19(3), 363–384. https://doi.org/10.1080/13658810412331317742

Luo, Y., Shen, M., Li, E., Xiao, Y., Wen, H., Ren, Y., & Xie, J. (2019). ur na l P of. Carbohydrate Polymers, 115713. https://doi.org/10.1016/j.carbpol.2019.115713

Malik, A., Maciejewski, R., Towers, S., McCullough, S., & Ebert, D. S. (2014). Proactive spatiotemporal resource allocation and predictive visual analytics for community policing and law enforcement. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1863–1872. https://doi.org/10.1109/TVCG.2014.2346926

Micheli, M., Gevaert, C. M., Carman, M., Craglia, M., Daemen, E., Ibrahim, R. E., Kotsev, A., Mohamed-Ghouse, Z., Schade, S., Schneider, I., Shanley, L. A., Tartaro, A., & Vespe, M. (2022). AI ethics and data governance in the geospatial domain of Digital Earth. Big Data and Society, 9(2). https://doi.org/10.1177/20539517221138767

Nwachukwu, M. A., Nwachukwu, J., Babatunde, A., Anyanwu, J., Ekweogu, C., & Nwachukwu, A. N. (2022). Geospatial Intelligence Training Concept for Terrorism Surveillance, Nigeria to Infusive Sub-Saharan African Countries. American Journal of Geospatial Technology, 1(1), 44–51. https://doi.org/10.54536/ajgt.v1i1.537

Pinto-Hidalgo, J. J., & Silva-Centeno, J. A. (2022). AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon Rainforest. Revista de Teledeteccion, 2022(59), 1–21. https://doi.org/10.4995/raet.2022.15710

Preye Winston Biu, Johnson Sunday Oliha, & Ogagua Chimezie Obi. (2024). the Evolving Role of Geospatial Intelligence in Enhancing Urban Security: a Review of Applications and Outcomes. Engineering Science & Technology Journal, 5(2), 483–495. https://doi.org/10.51594/estj.v5i2.826

Prof Dr. Aftab Ahmad Malik. (2023). The Modern Electronic and other Technologies to Combat New Wave of Terrorism and Criminal Activities. International Journal for Electronic Crime Investigation, 7(3), 8. https://doi.org/10.54692/ijeci.2023.0703156

Purbahapsari, A. F., & Batoarung, I. B. (2022). Geospatial Artificial Intelligence for Early Detection of Forest and Land Fires. KnE Social Sciences, 2022, 312–327. https://doi.org/10.18502/kss.v7i9.10947

Quinto-Sanchez, M., & Huerta-Pacheco, N. S. (2023). Missing persons patterns from Mexico: evidence of a forensic emergency crisis. Forensic Sciences Research, 8(4), 288–294. https://doi.org/10.1093/fsr/owad026

R M, Y., & Dolui, B. (2021). Statistical and machine intelligence based model for landslide susceptibility mapping of Nilgiri district in India. Environmental Challenges, 5(May), 100211. https://doi.org/10.1016/j.envc.2021.100211

Rachmi Azanisa Putri, Panca Hadi Putra, & Ryan Randy Suryono. (2023). The Integrated Information System Implementation Strategy in Korlantas Polri Based on the Zachman Framework Approach. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 7(2), 381–388. https://doi.org/10.29207/resti.v7i2.4842

Ray, C., Dréo, R., Camossi, E., Jousselme, A. L., & Iphar, C. (2019). Heterogeneous integrated dataset for Maritime Intelligence, surveillance, and reconnaissance. Data in Brief, 25. https://doi.org/10.1016/j.dib.2019.104141

Rokhsaritalemi, S., Sadeghi-Niaraki, A., & Choi, S. M. (2023). Exploring Emotion Analysis Using Artificial Intelligence, Geospatial Information Systems, and Extended Reality for Urban Services. IEEE Access, 11, 92478–92495. https://doi.org/10.1109/ACCESS.2023.3307639

Rossmo, D. K., & Harries, K. (2011). The Geospatial Structure of Terrorist Cells. Justice Quarterly, 28(2), 221–248. https://doi.org/10.1080/07418820903426197

Sandino, J., Pegg, G., Gonzalez, F., & Smith, G. (2018). Aerial mapping of forests affected by pathogens using UAVs, hyperspectral sensors, and artificial intelligence. Sensors (Switzerland), 18(4), 1–17. https://doi.org/10.3390/s18040944

Shapiro, A. (2019). Predictive policing for reform? Indeterminacy and intervention in big data policing. Surveillance and Society, 17(3–4), 456–472. https://doi.org/10.24908/ss.v17i3/4.10410

Shears, J. (2013). The new intelligence. GEO: Connexion, 12(10), 20–21. https://doi.org/10.1201/9781420013863.ch1

Shehu, A., Aliyu Kangiwa, H., & Sani, A. (2023). Remote Surveillance: a Means of Intelligence Gathering for Minimizing Security Challenges in Nigeria. Journal of Engineering Science, 29(4), 59–71. https://doi.org/10.52326/jes.utm.2022.29(4).15

Spaulding, J. S., & Morris, K. B. (2022). An optimised approach to near repeat analysis for intelligence driven crime linkage. Journal of Policing, Intelligence and Counter Terrorism, 17(1), 24–47. https://doi.org/10.1080/18335330.2021.1945663

Spiegel, S. J., Ribeiro, C. A. A. S., Sousa, R., & Veiga, M. M. (2012). Mapping spaces of environmental dispute: Gis, mining, and surveillance in the Amazon. Annals of the Association of American Geographers, 102(2), 320–349. https://doi.org/10.1080/00045608.2011.641861

Sufi, F. K., Alsulami, M., & Gutub, A. (2023). Automating Global Threat-Maps Generation via Advancements of News Sensors and AI. Arabian Journal for Science and Engineering, 48(2), 2455–2472. https://doi.org/10.1007/s13369-022-07250-1

Tsioufis, M., Fytopoulos, A., Kalaitzi, D., & Alexopoulos, T. A. (2024). Discovering maritime-piracy hotspots: a study based on AHP and spatio-temporal analysis. Annals of Operations Research, 335(2), 861–883. https://doi.org/10.1007/s10479-023-05352-z

Udochukwu, E., Christian, Su. I., & Adebayo, A. (2014). The Application of Geospatial Intelligence in National Security for Sustainable Development to combat Terrorism Insurgence in Nigeria. IOSR Journal of Environmental Science, Toxicology and Food Technology, 8(9), 11–16. https://doi.org/10.9790/2402-08931116

Veerasamy, N., Moolla, Y., & Dawood, Z. (2022). Application of Geospatial Data in Cyber Security. European Conference on Information Warfare and Security, ECCWS, 2022-June, 305–313. https://doi.org/10.34190/eccws.21.1.447

Zhao, M., & Liu, X. (2018). Development of decision support tool for optimizing urban emergency rescue facility locations to improve humanitarian logistics management. Safety Science, 102(September 2017), 110–117. https://doi.org/10.1016/j.ssci.2017.10.007

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Published

2025-02-28