spatial

Predicting Property Investment Opportunities in an Emerging Urban Neighborhood

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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 […]

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Predicting House Demand with Spatial Considerations in a Growing Suburb

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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

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Navigating the Expansive Horizon of Spatial Data Science

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By Shahabuddin Amerudin Abstract In recent times, the realm of spatial data science has witnessed an unprecedented surge, propelled by the exponential growth of spatial data and its potential applications across diverse domains. This review article delves into the multifaceted world of spatial data science, spanning its foundational principles, practical applications, inherent challenges, and the evolving research trends that are shaping its trajectory. By exploring the intricate interplay of spatial data, complexities, and novel methodologies,

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Unveiling Spatial Relationships: Predictive Applications of Regression Analysis

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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

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Spatial Analysis Techniques for Unveiling Geographic Patterns and Interactions

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By Shahabuddin Amerudin Introduction Spatial analysis is a critical discipline within geography and various other fields that deal with spatial data. It involves the examination of geographic patterns, relationships, and dependencies among data points in a given space. This exploration is crucial for understanding the underlying mechanisms driving spatial phenomena and for making informed decisions in urban planning, environmental management, economics, and various other domains. In this article, we delve into several key techniques of

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Revisiting the Relevance of Key Skills for GIS Software Developers in the Current Technological Landscape: A Review of Justin Holman’s 2012 Spatial Career Guide

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Suggestion for Citation: Amerudin, S. (2023). Revisiting the Relevance of Key Skills for GIS Software Developers in the Current Technological Landscape: A Review of Justin Holman’s 2012 Spatial Career Guide. [Online] Available at: https://people.utm.my/shahabuddin/?p=6350 (Accessed: 12 April 2023).

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Spatial Career Guide – 5 Key Skills for Future GIS Software Developers – A Short Review

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Suggestion for Citation: Amerudin, S. (2023). Spatial Career Guide – 5 Key Skills for Future GIS Software Developers – A Short Review. [Online] Available at: https://people.utm.my/shahabuddin/?p=6339 (Accessed: 12 April 2023).

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Geographically Weighted Regression (GWR)

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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

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The Green Building Index (GBI) Certification 

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Introduction Green Building Index (GBI) is a rating system that evaluates the environmental performance of buildings in Malaysia. Developed by the Malaysia Green Building Confederation (MGBC), the system aims to promote sustainable building practices and reduce the environmental impact of buildings. GBI assesses buildings based on nine categories: energy efficiency, indoor environment quality, materials and resources, site and surrounding, water efficiency, innovation, environmental management, land use and ecology, and emissions and effluents. Each category is

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Spatial vs Geospatial [2]

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Mike Goodchild believes that we should make a distinction between spatial and geospatial believing that if spatial is special then geospatial is even more special! The way he sees it is that geospatial is a subset of something much larger that encompases any spatiotemporal frame, any spatial resoultion, non-Cartesian spaces and metrics and so on.  Spatial represents the big picture while geospatial carves out its own area of interest at on on the earth’s surface  He goes

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Spatial vs Geospatial [1]

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Often my students ask about the difference(s) between spatial and geospatial. These two words appear very frequently in remote sensing and GIS literature. The word spatial originated from Latin ‘spatium’, which means space. Spatial means ‘pertaining to space’ or ‘having to do with space, relating to space and the position, size, shape, etc.’ (Oxford Dictionary), which refers to features or phenomena distributed in three-dimensional space (any space, not only the Earth’s surface) and, thus, having

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