(Data science method) algorithm, field research, bottom-up analysis, the end of theory
Kitchin (2014) discusses the new types of empiricism and the declaration of the “end of theory” through the utilization and adoption of data-driven approaches in science and the advancement of digital humanities and computational social sciences that promote the radically diverse techniques in making sense of the culture, history, economy, and society. The scholar argues that the use of data-driven approaches is consistent with the tenets of scientific approaches and simultaneously ensures the openness in the use of the hybrid combination of inductive and deductive approaches to augment the understanding of the phenomenon. The utilization of big data is made possible by the data analytic tools that help understand the data and interpret the information. According to the articles, the data science adoption shall be the end of theoretical epistemologies in research.
Anderson, C. (2006). The end of theory: The data deluge makes the scientific method obsolete. Wired magazine, 16–07.
1: Begin by writing a comprehensive introduction to your selected human development topic. Identify salient harms, quantify their significance and describe the inherent and complex nature of the geospatial human development process you are investigating. Identify the geospatial data science method you have selected and describe it. State your broad central research question and relate how your selected method has been used to describe, analyze and/or model your selected geospatial human development process.
2: Select the type of inquiry your investigation into a geospatial human development process will seek to answer. Consider the following categorical types of inquiries into human development (see <link is hidden> />
– 2a: An exploratory inquiry seeks to find out what is happening through investigating a social process or phenomenon as a kind of developmental puzzle. Your research focuses on how a process or system has developed and as part of that exploratory inquiry you are likely to generate sub-questions to consider for additional research.
– 2b: A descriptive inquiry seeks to provide a profile of individuals, events or situations and can be considered as a kind of mechanical puzzle that describes how something works and why it works in this way. Measures of accessibility or level-of-service are evaluative planning tools that could serve to describe a current state.
– 2c: An explanatory inquiry seeks to understand what is happening in a situation or problem by identifying causes and effects. This explanation may involve knowledge about: why certain events take place; why things happen as they do; how things happen; and what are the processes involved.
– 2d: An explanatory inquiry can be a kind of comparative puzzle where considering the similarities and differences between processes or contexts can be useful to understand systematic relationships.
– 2e: An evaluative inquiry seeks to grade or make judgement as to the effectiveness of a particular practice. It can be a kind of causal / predictive puzzle that explores the influence of a particular factor on another factor or explores causes underlying an observed phenomenon or process.
Support how your selected type of inquiry relates to your broader central research question in terms of scope, processes, hierarchy or dynamics. Permutate through the details of your human development process in order to identify, describe, analyze and possibly infer inherent, systematic and native intricacies. Identify at least three sub-research questions that are explicit and direct in their service of answering your broader themed focus, while also being proximate and germane to one another. Also support how your selected type of inquiry relates to your focused sub-research questions, again in terms of scope, processes, hierarchy or dynamics.
3. Select two geospatial data science methods from your eight sources. Describe the data sources used with each of the applied scientific methods and identify tools or methods the authors used to collect their data. How was the data processed? Did the authors conduct a survey or use secondary data? Did their data have both a spatial and temporal dimension? What is the validity and reliability of the data? Add a table, chart, illustration or map in order to further reinforce your description of the data.
4. Computationally describe what each of the two data science methods does and why each one is significant towards advancing a better understanding of your selected human development process. Describe how the authors used their data to specify their statistical model. Identify and describe the variables used in each model. Compare and evaluate the two geospatial data science methods as needed. Is one an improvement over the other through addressing a research gap in the literature? Are the two methods complimentary in answering more detailed aspects of your broader research focus? Typeset each of the two methods in their mathematical form. Again, add a table, chart, illustration or map in order to describe computational analysis and/or results.
5. Identify the findings that each article reports as a result of applying their statistical method to the data. Describe each finding and explain how it contributes towards answering part of your central research question. Did the authors note significant correlations between two or more variables during their analysis? Feel free to speculate and discuss as to why any correlations may exist. Include non-textual elements, such as plots, charts, tables and/or maps to illustrate key findings from each of your journal articles as needed. Do any of the models have predictive power to forecast future conditions? Have the authors validated their model? How did they do so?
6. Identify an area in the research that appears to need further consideration yet seems to have been neglected. Perhaps it is a part of your central research question that was left unanswered. A research gap could also reflect a limitation or need for improvement with a statistical method. Do the data or models fail to describe, analyze or predict some essential element from your area of research? Identify a research gap and discuss it in your conclusion.