Spatial Aggregation Entropy: A Heterogeneity and Uncertainty Metric of Spatial Aggregation

Xiao, Jia

doi:10.1080/24694452.2020.1807309

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…This limitation corresponds to the well-known Modifiable Area Unit Problem (MAUP), referred to in studies on the geography of crime [90,91]. Although there is a large body of literature on this problem, there is no general solution to it [92]. A sensitivity analysis is recommended for future studies, which was not possible in this study due to the lack of data at finer spatial scales.…”

Section: Discussionmentioning

confidence: 99%

Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal

Tavares

Costa

2021

IJGI

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Many researchers have unraveled innovative ways of examining geographic information to better understand the determinants of crime, thus contributing to an improved understanding of the phenomenon. Property crimes represent more than half of the crimes reported in Portugal. This study investigates the spatial distribution of crimes against property in mainland Portugal with the primary goal of determining which demographic and socioeconomic factors may be associated with crime incidence in each municipality. For this purpose, Geographic Information System (GIS) tools were used to analyze spatial patterns, and different Poisson-based regression models were investigated, namely global models, local Geographically Weighted Poisson Regression (GWPR) models, and semi-parametric GWPR models. The GWPR model with eight independent variables outperformed the others. Its independent variables were the young resident population, retention and dropout rates in basic education, gross enrollment rate, conventional dwellings, Guaranteed Minimum Income and Social Integration Benefit, purchasing power per capita, unemployment rate, and foreign population. The model presents a better fit in the metropolitan areas of Lisbon and Porto and their neighboring municipalities. The association of each independent variable with crime varies significantly across municipalities. Consequently, these particularities should be considered in the design of policies to reduce the rate of property crimes.

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Section: Discussionmentioning

confidence: 99%

Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal

Tavares

Costa

2021

IJGI

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“…The results show that even in the absence of spatial autocorrelation, the correlation coefficient increases by grouping and aggregation. Attempts have been made to provide solutions for the MAUP problem based on the size and interconnectedness of the areas [66] and spatial entropy [67,68].…”

Section: Scalementioning

confidence: 99%

Machine Learning of Spatial Data

Nikparvar

Thill

2021

IJGI

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Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue that they offer the most promising prospects for the future of spatial machine learning.

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“…Planning has traditionally been associated with a reductionist perspective of cause and effect (Byrne, 2003; Chettiparamb, 2014) – a perspective that essentially delimits itself to an understanding of mechanical complicatedness, more so than social complexity. However, aggregating and simplifying data across spatial scales causes issues with oversimplification and loss of statistical generalizability (Dapena et al, 2016; Garreton and Sánchez, 2016; Jelinski and Wu, 1996; Kar and Hodgson, 2012; Stillwell et al, 2018; Xiao, 2021). This discrepancy is the entry point to the research question of this paper, which concerns a basic premise of planning: i.e., how abstract can a political decision-making model be without compromising its generalizability?…”

Section: Introductionmentioning

confidence: 99%

Scale-dependent complexity in administrative units and implications for data-driven decision-making models

Søder

2023

Planning Theory

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Through analysis and discussion of basic systemic properties of a rural municipality, this paper explores how aggregating data in planning and land use modeling can potentially obscure intricate real-world behavior. Complexity theory is applied as a theoretical framework for explaining this hypothesis. Thus, the aim of this study is to address the author’s desire to understand systemic complexity when designing a data-driven decision-making model for rural planning. The novelty of this approach is two-fold: one, most studies on scalability issues in planning addresses spatial complexity, more so than systemic complexity within the complex system that the very act of planning strives to dictate. Two, although delimited to the scope of the study, the accessibility to and use of complete and valid socio-demographic data enables a rarely demonstrated accurate representation of an entire population. It is ultimately observed that on the disaggregated municipal level, systemic dispersion increases parallelly with population size, a correlation that is significantly influenced by gender ratio in any given parish – a characteristic that was not visible at the aggregated municipal level. In addition to advancing the understanding and placement of complexity science within spatial data science, these insights will make it easier to assess the generalizability of any given administrative unit by quantifying basic complexity attributes; in this case based on the correlation dispersion caused by the fragmentation of a municipality into its comprising parishes.

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Spatial Aggregation Entropy: A Heterogeneity and Uncertainty Metric of Spatial Aggregation

Cited by 7 publications

References 29 publications

Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal

Spatial Modeling and Analysis of the Determinants of Property Crime in Portugal

Machine Learning of Spatial Data

Scale-dependent complexity in administrative units and implications for data-driven decision-making models

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