Are Regional Differences in Psychological Characteristics and Their Correlates Robust? Applying Spatial-Analysis Techniques to Examine Regional Variation in Personality

There is growing evidence that psychological characteristics are spatially clustered across geographic regions and that regionally aggregated psychological characteristics are related to important outcomes. However, much of the evidence comes from research that relied on methods that are theoretically ill-suited for working with spatial data. The validity and generalizability of this work are thus unclear. Here we address two main challenges of working with spatial data (i.e., modifiable areal unit problem and spatial dependencies) and evaluate data-analysis techniques designed to tackle those challenges. To illustrate these issues, we investigate the robustness of regional Big Five personality differences and their correlates within the United States (Study 1; N = 3,387,303) and Germany (Study 2; N = 110,029). First, we display regional personality differences using a spatial smoothing approach. Second, we account for the modifiable areal unit problem by examining the correlates of regional personality scores across multiple spatial levels. Third, we account for spatial dependencies using spatial regression models. Our results suggest that regional psychological differences are robust and can reliably be studied across countries and spatial levels. The results also show that ignoring the methodological challenges of spatial data can have serious consequences for research concerned with regional psychological differences.

Modifiable Areal Unit Problem (MAUP): Analysis of Agriculture of the State of Paraná-Brazil

The way the researcher groups his research data will influence the result of his work. In the literature, this phenomenon is treated as a Problem of the Modifiable Areal Unit. The objective of this article was to analyze the three spatial levels by Municipalities, Regional Centers and Mesoregions using the following data: gross domestic product, effective agricultural production, grain production and gross value of agricultural production for the state of Paraná-Brazil in the period since 2012 until 2015. The methodological procedure studied data from the Paranaense Institute for Economic and Social Development of the above-named variables collected on the website of the Paranaense Institute for Economic and Social Development of the 399 municipalities, 23 regional centers and 10 mesoregions. The results found show the presence of the Modifiable Areal Unit Problem, presenting different results for each level of grouping. The study revealed the problem of the modifiable areal unit is a relevant occurrence and it should be disregarded by researchers who work with clusters of spatial data in their studies. The results found allow a better understanding of the scale effect and demonstrate the efficiency of spatial analysis in socioeconomic data.

Quantifying the impacts of the modifiable areal unit problem in travel demand models

This project introduces new analyses of the impacts of the modifiable areal unit problem (MAUP) in traffic assignment models which are not widely available in the literature, as well as to reveal how stable the effects are in diverse models. A comprehensive review of the literature is conducted to provide an overview of MAUP, including the scale and zonal effects, as well as its recent applications in travel demand modelling and other subject areas. Particular scrutiny is made towards inappropriate methods of MAUP-analysis in travel demand models. The scale effect is tested in traffic assignment models using associated zone structures of the Greater Montreal Area (GMA), a unique geographic region involving island regions and water bodies.

Quantifying the impacts of the modifiable areal unit problem in travel demand models

This project introduces new analyses of the impacts of the modifiable areal unit problem (MAUP) in traffic assignment models which are not widely available in the literature, as well as to reveal how stable the effects are in diverse models. A comprehensive review of the literature is conducted to provide an overview of MAUP, including the scale and zonal effects, as well as its recent applications in travel demand modelling and other subject areas. Particular scrutiny is made towards inappropriate methods of MAUP-analysis in travel demand models. The scale effect is tested in traffic assignment models using associated zone structures of the Greater Montreal Area (GMA), a unique geographic region involving island regions and water bodies.

Bus Service Level and Horizontal Equity Analysis in the Context of the Modifiable Areal Unit Problem

The modifiable areal unit problem is of great importance in geographic science. The use of a specific zoning impacts the social and economic imbalances that can be generated in the deployment of services, facilities, and infrastructure. In this article, GIS is used together with simulation and optimization tools to analyse the effects of bus frequency changes in the levels of service and horizontal equity derived from different types of territorial zoning. The city of Palma (Balearic Islands, Spain) was chosen as a case study for the method, for which different geographical areas are used: neighbourhoods, census sections, cadastral blocks, and a 400 x 400 m mesh. The results show significant variations of the optimal frequencies obtained, depending on the type of zoning used. In general, smaller zonings show much higher sensitivity for the detection of imbalances between the population and bus service level. Likewise, orthogonal zonings also prove useful for identifying service and population concentration over other zonings. The use of large spatial units could lead to the misdiagnosis of needs and the implementation of actions that do not actually improve the level of service or the equity of the transport service. It is recommended to consider combining zonings of different sizes simultaneously, in order to accurately highlight imbalances and to argue for transport service improvements.

Exploring Residential Heterogeneity through Multiscalar Lens: A Case Study of Hangzhou, China

The pattern, process, and mechanism of residential heterogeneity vary significantly with different geographical scales. However, most traditional methods ignore the checkboard and modifiable areal unit problem (MAUP), which may cover up the complexity and hierarchy of social space. Taking Hangzhou city as an example, a multiscalar method was proposed based on the information entropy theory to estimate residential heterogeneity and its scale sensitivity. Based on the sixth population census of Hangzhou and the housing price database of 6,536 residential districts from 2008 to 2018, we explore the scale effect and dynamic characteristics of residential heterogeneity. The results of spatial simulation and geostatistical analysis based on Python Spatial Analysis Library (PySAL) module show that the multiscalar algorithm better presents the real segregation pattern than traditional method, which is one of the new models and technologies in urban geography complex system. Exploring residential heterogeneity through multiscalar lens provides an important basis for the gradual and refined urban renewal.