Listing 1 - 2 of 2 |
Sort by
|
Choose an application
Forecasting macroeconomic variables is key to developing a view on a country's economic outlook. Most traditional forecasting models rely on fitting data to a pre-specified relationship between input and output variables, thereby assuming a specific functional and stochastic process underlying that process. We pursue a new approach to forecasting by employing a number of machine learning algorithms, a method that is data driven, and imposing limited restrictions on the nature of the true relationship between input and output variables. We apply the Elastic Net, SuperLearner, and Recurring Neural Network algorithms on macro data of seven, broadly representative, advanced and emerging economies and find that these algorithms can outperform traditional statistical models, thereby offering a relevant addition to the field of economic forecasting.
Forecasting --- Intelligence (AI) & Semantics --- Forecasting and Other Model Applications --- Neural Networks and Related Topics --- Technological Change: Choices and Consequences --- Diffusion Processes --- Machine learning --- Economic Forecasting --- Artificial intelligence --- Economic forecasting --- Technology --- United States
Choose an application
Qualitative GIS is a mixed methods framework for social and spatial research. This entry provides a critical review of qualitative GIS and demonstrates how forms of evidence and analysis familiar to qualitative research have been integrated with GIS in a variety of ways in spatial research since the turn of the 21st century. The entry begins by discussing its epistemological foundations. Then, it describes the range of ways that researchers have integrated qualitative, quantitative, and geovisual methods in GIS. A first wave of qualitative GIS approaches in the 2000s developed four main strategies implementing qualitative forms of representation or analysis into GIS-based research: transformation of qualitative data for geovisualization, multimedia techniques for incorporating qualitative artifacts, mixed methodological frameworks, and software-level adaptations. These different practices of qualitative GIS have generated stronger and more nuanced social and spatial understanding than are possible within singular epistemological/methodological frameworks. This entry also discusses new approaches to qualitative GIS that are emerging through ongoing development in the form and function of spatial data and technologies. These developments are creating unique opportunities for qualitative GIS as a form of multimodal knowledge production in spatial research, as scholars bring together new types of qualitative data (spatial big data), analysis (algorithmic approach and digital ethnography), new visualization practices (qualitative geovisualization), and new digital technologies (locative apps).
Listing 1 - 2 of 2 |
Sort by
|