prediction

Predicting Property Investment Opportunities in an Emerging Urban Neighborhood

By • • Knowledge, Research, Teaching • , , , • 3 minutes of reading

By Shahabuddin Amerudin Introduction You are a real estate investor looking to identify promising property investment opportunities in an emerging urban neighborhood. To make informed decisions on whether to invest in land, shops, or houses, you need to predict their potential future value and assess their investment viability. This scenario explores how to predict property investment opportunities in such a dynamic urban environment. Defining the Objective The objective is to predict the future value and investment potential of properties in the urban neighborhood over the next five years. This includes forecasting property values and assessing the expected return on investment […]

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Predicting House Demand with Spatial Considerations in a Growing Suburb

By • • Knowledge, Research, Teaching • , , , , , , , • 4 minutes of reading

By Shahabuddin Amerudin Introduction As a real estate developer planning to invest in a growing suburban area, you recognize that housing demand is not solely influenced by time-related factors but also by spatial considerations. To make precise predictions about where and when houses will be in demand, you need to incorporate both temporal and spatial elements into your forecasting. Defining the Objective The objective remains to forecast the demand for houses in the suburban area over the next five years, but now with a spatial dimension. You want to estimate the number of new homes that potential buyers are likely

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Mastering Forecasting: Techniques for Predicting Condition Fulfillment and Target Achievement

By • • Knowledge, Research, Teaching • , , , , , • 3 minutes of reading

By Shahabuddin Amerudin Introduction In today’s data-driven world, forecasting has become a cornerstone of decision-making. Whether it’s predicting the fulfillment of a specific condition or meeting a target, the ability to make accurate predictions is a critical skill. This article delves deep into the art of forecasting, focusing on conditions and targets, and explores various methodologies with real-world examples to illustrate their effectiveness. Defining the Objective Every successful forecasting project begins with a clearly defined objective. Consider a retail store aiming to forecast whether they will meet their monthly sales target. In this scenario, the objective is straightforward: predict whether

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Unveiling Spatial Relationships: Predictive Applications of Regression Analysis

By • • Knowledge, Research, Teaching • , , , , , , , • 6 minutes of reading

By Shahabuddin Amerudin Introduction In the realm of data analysis, regression analysis stands as a powerful tool that facilitates the exploration, understanding, and prediction of spatial relationships. By unraveling the intricate connections between variables, it provides insights into the factors driving observed spatial patterns. In this article, we delve into the fascinating world of regression analysis, focusing on its predictive applications through two distinct examples: the prediction of human deaths and the analysis of grave demand. Regression analysis forms the cornerstone of modern statistical analysis, enabling us to move beyond mere correlation and into the realm of causation. As we

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Geographically Weighted Regression (GWR)

By • • Public, Research, Teaching • , , , , • 11 minutes of reading

Geographically Weighted Regression (GWR) is a spatial statistical method used for predicting outcomes based on geographical data. To conduct prediction using GWR, you can follow these steps: Note: It is essential to validate the GWR results with independent validation data and assess the model performance using appropriate validation metrics. Geographically Weighted Regression (GWR) is a powerful statistical tool for predicting outcomes based on geographical data. Its ability to account for spatial heterogeneity in the relationships between independent and dependent variables makes it an attractive alternative to traditional regression methods such as Ordinary Least Squares (OLS). The quality of GWR results

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Space Demand Analysis for Muslim Cemeteries: Methods, Techniques, and Expectations

By • • Knowledge, Personal, Project, Public, Research, Teaching • , , , , , , • 26 minutes of reading

Introduction Space demand analysis is a critical process that helps organizations, developers, and architects to determine the amount of space needed for a particular function or activity. The process involves identifying the space requirements of an organization, project, or event, and then determining the amount of space necessary to meet those requirements. This analysis is important for ensuring that the space is efficient, functional, and cost-effective. Space demand analysis for cemeteries is a process used to evaluate the amount of land and burial plots needed to accommodate the deceased. It involves identifying the space requirements of a particular cemetery, and

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