Exploratory Spatial Data Analysis of Traffic Forecasting: A Case Study

Transportation planning has historically relied on statistical models to analyze travel patterns across space and time. Recently, an urgency has developed in the United States to address outdated policies and approaches to infrastructure planning, design, and construction. Policymakers at the federal, state, and local levels are expressing greater interest in promoting and funding sustainable transportation infrastructure systems to reduce the damaging effects of pollutive emissions. Consequently, there is a growing trend of local agencies transitioning away from the traditional level-of-service measures to vehicle miles of travel (VMT) measures. However, planners are finding it difficult to leverage their investments in their regional travel demand network models and datasets in the transition. This paper evaluates the applicability of VMT forecasting and impact assessment using the current travel demand model for Dane County, Wisconsin. The main finding is that exploratory spatial data analysis of the derived data uncovered statistically significant spatial relationships and interactions that planners cannot sufficiently visualize using other methods. Planners can apply these techniques to identify places where focused VMT remediation measures for sustainable networks and environments can be most cost-effective.

EXPLORATORY SPATIAL DATA ANALYSIS (ESDA) ON COVID-19 CASES IN MALAYSIA

The Malaysian government implemented The Movement Control Order (MCO) on 18 March 2020 to control the spread of the COVID-19 outbreak. However, the third wave that started in September 2020 during the Recovery Movement Control Order (RMCO) phase saw a continuous increase in the number of cases. In this study, the exploratory spatial data analysis (ESDA) was used to analyse the existence of COVID-19 spatial clusters. Moran's index was used to map the spatial autocorrelation (cluster) to showcase the spreading patterns of the COVID-19 pandemic in Malaysia. The study results indicated significant changes in the COVID-19 hotspots over time. At the beginning of 2020, the state of Selangor and Sarawak were the first locality to become a significant COVID-19 hotspot. Furthermore, this research showed all affected areas during the study period. Overall, a non-random distribution of COVID-19 occurrences was detected, thus suggesting a positive spatial autocorrelation. Many parties are affected by the COVID-19 pandemic, especially those involved in healthcare provision, financial assistance allocation, and law enforcement. Other sectors such as the economy, education, and religion are also affected. Therefore, the findings from this study will provide useful information to all the related governmental and private agencies, as well as policymakers and researchers.

Dependencia espacial de la tasa municipal de desempleo en Andalucía. Un análisis espacial exploratorio

El presente trabajo examina la dependencia espacial, global y local, de la Tasa Municipal de Desempleo en Andalucía. Aplicando el software Geoda realizamos en primer lugar un ejercicio exploratorio encaminado a identificar los valores atípicos superiores del indicador. Posteriormente se implementan estadísticos propios del Análisis Espacial Exploratorio para determinar la dependencia espacial del desempleo en Andalucía. En la última sección de resultados se identifican los puntos calientes (Hot Spots) de mayor incidencia en la construcción del indicador de autocorrelación espacial. Se concluye tras el examen de resultados en la necesidad de añadir el territorio entre las variables de análisis para el estudio sociológico del paro.The current paper examines the spatial, global and local autocorrelation of the Municipal Unemployment Rate in Andalusia. Applying the Geoda software, firstly, we carried out an exploratory exercise aimed at identifying the superior outliers of the indicator. Later, statistics of the Exploratory Spatial Data Analysis are implemented to determine the spatial dependence of unemployment in Andalusia. In the last section of results, the hot spots with the highest incidence in the construction of the spatial autocorrelation indicator are identified. After examining the results it’s concluded on the need to add the spatial context among the analysis variables for the sociological research of unemployment.

Study of Rainfall and Water Discharge Spatial Variability Using Exploratory Spatial Data Analysis Method in Bondowoso Regency

The difference in rainfall intensity affects the hydrological cycle as a process that greatly determines the amount of water discharge. Thus, in water resources management, it is important to determine the distribution pattern of rainfall and discharge. By studying the characteristics of rainfall distribution patterns and water discharge, the potential of water resources can be illustrated well. This study uses the Exploratory Spatial Data Analysis method to examine spatial variability of rainfall intensity and water discharge in Bondowoso Regency. Rainfall and discharge data are collected from 35 rain stations and 227 weirs in 2008 until 2018. This study produces monthly average rainfall distribution values between 190 mm / month with monthly average discharge between 7300lt/sec/month. Meanwhile, the obtained average annual rainfall distribution values are between 2300 mm/year with annual average discharge values between 105000 lt/sec/month. The spatial distribution map using IDW method produces information on the potential of water resources as follow: the higher the height of a place, the higher the average monthly rainfall, while the lower the height of a place, the higher the average monthly discharge. As for the obtained correlation value between rainfall and discharge is R² = 0.665.

Análisis espacial de la vulnerabilidad socioeconómica de la ciudad de Luján (Argentina): interpretación modelística a partir de su mapa social para la identificación de áreas prioritarias de planificación

El artículo presenta el análisis de la distribución espacial de la vulnerabilidad socioeconómica en la ciudad de Luján y su interpretación modelística considerando su mapa social con la finalidad de identificar áreas de planificación. Se utilizan metodologías de análisis multivariado mediante el uso de Sistemas de Información Geográfica para la construcción de áreas diferenciales y el Análisis Exploratorio de Datos Espaciales (ESDA, Exploratory Spatial Data Analysis) que permite medir la correlación como indicador de la asociación espacial entre ambos componentes. La importancia de esta línea de aplicaciones radica en generar herramientas de la Geografía Aplicada en apoyo al Ordenamiento Territorial.