This study aims to find out whether exchange rate volatility affects real estate domestic house prices in Ghana. To this end, a 32 years secondary data from World Development Indicators (WDI) and data from Real Estate Developers in Ghana are employed for the study. The study employs Autoregressive distributed lags (ARDL) bounds testing of cointegration t o test the null hypothesis that exchange rate volatility has n o impact on real estate housing prices. The study finds that real estate price is cointegrated with remittances, exchange rate and inflation. The long run equilibrium is stable and significant. Exchange rates d o not cause changes in real estate prices in both short and long run. Similarly past prices of real estate d o not have impact on current house prices. Rather, remittances positively cause real estate prices. Inflation on its part has a negative impact on real estate prices. It is therefore concluded that, volatility in the exchange rate between the cedi and other trading currencies does not predict changes in real estate prices.
Crime activities have long been a great concern of all the countries. Analysis of crime data has been a key part yet a considerable challenge for discovering crime patterns and reducing crimes. In recent year, along with the development of data collection and data mining techniques, lots of big data-related studies have been conducted to analyze the crime data. Studying the numerical influential factors is one important yet challenging problem, especially for those indirect features. Though a number of studies have been conducted to analyze the influential factors of crime activities, most of them have some limitations in the era of ''big data''. Some adopted the linear statistical methods, of which the basic assumption is opposite to the non-linear real world. Some limited their studied factors within one or two aspects. Some overlooked the importance of ranking the influence of factors. To fill these research gaps, this paper proposes a big data approach to analyze the influential factors on the crime activities, and experimented it on New York City. More than 1515 different factors ranging from demographic, housing, education, economy, social, and city planning were considered and analyzed. The proposed framework combines non-linear machine learning algorithms and geographical information system (GIS) to study the spatial determinants of crimes. Recursive feature elimination (RFE) is used to select the optimum feature set. Performance of gradient boost decision tree (GBDT), logistic regression (LR), support vector machine (SVM), artificial neural network (ANN) and random forest (RF) are compared to generate the optimum model. Important impact factors were then investigated using GBDT and GIS. The experimental results demonstrate that the combined GBDT and GIS model can find out the most important factors of crime rate with high efficiency and accuracy.INDEX TERMS Big data techniques, feature analysis, felony assault, gradient boost decision tree, machine learning, recursive feature elimination.
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