Introduction
Having access to large datasets enhances geographic analysis. Consequently, this leads to a better understanding of environmental and social phenomena. Furthermore, it explains urban dynamics. Hence, instead of providing a proximate analysis of these phenomena, a more comprehensive geographic analysis is presented based on the wide range of data provided. This makes the relevant decisions made more realistic. Also, planning becomes more context-relevant. This article highlights the major influence of large datasets on geographic analysis. Moreover, examples of enhanced understanding developed due to dependence on large datasets are provided. This is concluded by exploring factors leading to the implementation of this advancement in geographic analysis.
Influence of Large Datasets on Geographic Analysis
Long-term changes can be perceived when exploring large datasets. This leads to a better analysis of dynamics over time. Also, the limitations of static and traditional approaches are exceeded. Reliance on large datasets also leads to real-time special analytics. Hence, decisions made based on the data collected become more accurate. The Geographic Information System (GIS) has been enhanced due to improvements in big data analysis techniques. Therefore, the tools provided turned into more sophisticated platforms that analyze huge datasets. Additionally, more sophisticated models can be created when collecting data using several resources. These models lead to a better understanding of spatial issues due to comprehending complex cause-and-effect relationships.
Enhanced Understanding Resulting from Large Datasets Analysis
There are three major examples elaborating on the impact of large datasets on geographic analysis. First, the large datasets analysis results in environmental monitoring since changes in various environmental indicators, e.g., carbon storage, can be tracked. Hence, these changes can be understood, and the drives leading to them can be identified. Second, complex urban dynamics can be understood when analyzing large datasets, so that planners can provide more sustainable and resilient urban development. Finally, social equity can be attained via analyzing accessibility to services among demographic groups of the community. Hence, accurate decisions can be made accordingly.
Factors Enhancing Large Datasets Accessibility
More studies can be conducted based on the availability of large datasets. When open-source data are available, more studies can be conducted. For instance, transportation network data availability can enhance spatial accessibility studies. More complex analysis can be conducted when using more advanced tools, as well. Finally, reliance on artificial intelligence (AI), represented in machine learning (ML) and natural language processing (NLP), leads to processing and integrating large sets of geospatial data. User-friendly interfaces can be created to enable non-experts to extract insights easily.
Conclusion
Several geographic decisions can be made when having access to large datasets. Some of these decisions are related to environmental monitoring. Others are related to urban dynamics. The third category is related to achieving social equity. However, these advancements are attained via having access to open-source data and the implications of AI in this area.
