Designing a virtual model of a real city based on wayfinding strategies and difficulties of the users

Peer Reviewe

Designing a virtual model of a real city based on wayfinding strategies and difficulties of the users

Peer Reviewe

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

Urbanization often occurs in an unplanned and uneven manner, resulting in profound changes in patterns of land cover and land use. Understanding these changes is fundamental for devising environmentally responsible approaches to economic development in the rapidly urbanizing countries of the emerging world. One indicator of urbanization is built-up land cover that can be detected and quantified at scale using satellite imagery and cloud-based computational platforms. This process requires reliable and comprehensive ground-truth data for supervised classification and for validation of classification products. We present a new dataset for India, consisting of 21,030 polygons from across the country that were manually classified as “built-up” or “not built-up,” which we use for supervised image classification and detection of urban areas. As a large and geographically diverse country that has been undergoing an urban transition, India represents an ideal context to develop and test approaches for the detection of features related to urbanization. We perform the analysis in Google Earth Engine (GEE) using three types of classifiers, based on imagery from Landsat 7 and Landsat 8 as inputs. The methodology produces high-quality maps of built-up areas across space and time. Although the dataset can facilitate supervised image classification in any platform, we highlight its potential use in GEE for temporal large-scale analysis of the urbanization process. Our methodology can easily be applied to other countries and regions

The Association between Land-Use Distribution and Residential Patterns: the Case of Mixed Arab-Jewish Cities in Israel

The emergence of GIS and the availability of high resolution geographic data have improved our ability to investigate the residential segregation in cities and to identify the temporal changes of the spatial phenomena. Using GIS, we have quantitatively and visually analyzed the correspondence between land-use distribution and Arab residential patterns and their changes in the period between 1983 and 2008 in five mixed Arab-Jewish Israeli cities. Results show a correspondence between the dynamics of Arab/Jewish residential patterns and the spatial distribution of various land-uses. Arab residential patterns diffused faster towards areas with relatively inferior land-uses than towards areas with more attractive land-uses, in which a gentrification process occurred. Moreover, large-scale non-residential land-uses act as spatial partitions that divide between Arab and Jewish residential areas. Understanding the association between the urban environment and residential patterns can help in formulating an appropriate social and spatial policy concerning planning of land-uses and design of the built environment in mixed cities

“Perceived neighbourhood” and tolerance relations: the case of Arabs and Jews in Jaffa, Israel

<p>This paper examines the complex social reality of mixed ethnic residential areas, as reflected in drawn perceived neighbourhoods of Jews and Arabs living in homogenous and mixed neighbourhoods in Jaffa, Tel Aviv. Through in-depth interviews conducted among 89 Jaffa's residents, it illustrates how different perceptions about the residential area and its residents (including attitudes, emotions, feeling of fear/safety, collective identity and tolerant relations) are reflected in the form, size and consensus area of residents' perceived neighbourhoods. The study finds a clear association between tolerant attitudes and the size and consensus area of the perceived neighbourhoods. Arabs perceive their neighbourhood as significantly larger than Jews do and are characterised by a larger consensus area. These findings are explained by the Arab's strong social cohesion, common national identity, rootedness in Jaffa, as well as by tolerant attitudes towards Jaffa's Jewish population. The study demonstrates the interrelation between the spatial, social and perceptual dimensions associated with the mixed residential area, and illustrates how these dimensions are reflected in drawn perceived neighbourhoods.</p

Assessing OpenStreetMap Completeness for Management of Natural Disaster by Means of Remote Sensing: A Case Study of Three Small Island States (Haiti, Dominica and St. Lucia)

Over the last few decades, many countries, especially islands in the Caribbean, have been challenged by the devastating consequences of natural disasters, which pose a significant threat to human health and safety. Timely information related to the distribution of vulnerable population and critical infrastructure is key for effective disaster relief. OpenStreetMap (OSM) has repeatedly been shown to be highly suitable for disaster mapping and management. However, large portions of the world, including countries exposed to natural disasters, remain incompletely mapped. In this study, we propose a methodology that relies on remotely sensed measurements (e.g., Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-2 and Sentinel-1) and derived classification schemes (e.g., forest and built-up land cover) to predict the completeness of OSM building footprints in three small island states (Haiti, Dominica and St. Lucia). We find that the combinatorial effects of these predictors explain up to 94% of the variation of the completeness of OSM building footprints. Our study extends the existing literature by demonstrating how remotely sensed measurements could be leveraged to evaluate the completeness of the OSM database, especially in countries with high risk of natural disasters. Identifying areas that lack coverage of OSM features could help prioritize mapping efforts, especially in areas vulnerable to natural hazards and where current data gaps pose an obstacle to timely and evidence-based disaster risk management

Public Open Spaces Evaluation Using Importance-Performance Analysis (IPA) in Saudi Universities: The Case of King Abdulaziz University, Jeddah

Public open spaces (POSs) provide multiple services (such as facilities for physical activities and social interactions) to local people, and these services are important for the well-being of society and for improving the quality of life. Extensive research on POSs has been carried out in developed countries (such as the US and Australia, as well as European countries including Spain, France, and Germany). However, POSs in the Saudi Arabian context remain unexplored. This study aims to examine the importance and performance of public open spaces on King Abdulaziz University (KAU) campus, Jeddah city, Saudi Arabia, using importance-performance analysis (IPA). One-way ANOVA and Kruskal&ndash;Wallis tests were performed to identify differences in the importance and performance of POSs. It was observed that there are significant differences between the importance and performance of public open spaces on the KAU campus, as perceived by stakeholders. Therefore, this study may be helpful in understanding the importance and performance of public open spaces, allowing spaces to be prioritized to improve management and restore open spaces to achieve environmental sustainability at a local scale. In addition, this study suggests that decision-makers involved in campus planning should consider the contribution of public open spaces to education, recreation, and the environment, at the campus planning stage

Utilizing publicly available satellite data for urban research: Mapping built-up land cover and land use in Ho Chi Minh City, Vietnam

Urbanization is a fundamental trend of the past two centuries, shaping many dimensions of the modern world. To guide this phenomenon and support growth of cities that are competitive and sustainably provide needed services, there is a need for information on the extent and nature of urban land cover. However, measuring urbanization is challenging, especially in developing countries, which often lack the resources and infrastructure needed to produce reliable data. With the increased availability of remotely sensed data, new methods are available to map urban land. Yet, existing classification products vary in their definition of “urban” and typically characterize urbanization in a specific point (or points) in time. Emerging cloud based computational platforms now allow one to map land cover and land use (LC/LU) across space and time without being constrained to specific classification products. Here, we highlight the potential use of publicly available remotely sensed data for mapping changes in the built-up LC/LU in Ho Chi Minh City, Vietnam, in the period between 2000 and 2015. We perform a pixel-based supervised image classification procedure in Google Earth Engine (GEE), using two sources of reference data (administrative data and hand-labeled examples). By fusing publicly available optical and radar data as input to the classifier, we achieve accurate maps of built-up LC/LU in the province. In today's era of big data, an easily deployable method for accurate classification of built-up LC/LU has extensive applications across a wide range of disciplines and is essential for building the foundation for a sustainable human society. Keywords: Urbanization, Built-up land cover, Landsat, Sentinel, Google Earth Engin