Is the Real Estate Market of New Housing Stock Influenced by Urban Vibrancy?

3noThe attractiveness and vibrancy of an urban area are very complex aspects that both Public Administrations and real estate developers and construction companies have to carefully consider in order to correctly address their investments and sustainable urban development projects. 'e aim of this paper is to study urban vibrancy and its relationship with the neighbourhood services and the real estate market of new housing stock. Spatial analyses are performed to study the influence of the Neighbourhood Services Index (NeSI) and its Principal Components (PCs) on listing prices and the construction activity. Spatial autoregressive (SAR) models are applied both with lattice data and data points, in order to manage spatial dependence and to identify the variables that significantly influence housing prices and construction site density. Findings highlight that the NeSI significantly influences the real estate market of new housing stock and that above the analyzed neighborhood services and the retail activities have a great, significant, and positive influence on the density of housing construction sites. 'e results of this study represent a real support for both public and private bodies to identify the most and least attractive and vibrant urban areas and to deal with important aspects of urban complexity.openopenBarreca, Alice; Curto, Rocco; Rolando, DianaBarreca, Alice; Curto, Rocco; Rolando, Dian

Urban Growth, Resident Welfare, and Housing Markets: Evidence from China

Cities have become the principal platform of economic growth and residents’ settlements. The importance of urban growth in residents’ quality of life is increasingly evident, because of the intimate interaction between urban characteristics and human activities. Cities shape urban residents’ welfare, bringing benefits and disadvantages as well. The complex way in which urban growth and residents’ activities interact makes it difficult to figure out which kinds of urban features are positively or instead negatively associated with residents’ quality of life. This puzzle makes it harder to clarify whether cities can provide enough net welfare to their residents. Thus, there is a major challenge to understand and evaluate how and to what extent urban growth shapes the welfare of urban residents. The link between cities and welfare has been hotly debated, yet we still have a limited grasp of how urban growth may impact residents’ welfare. Most studies concentrate on a certain dimension of urban welfare and a specific geographical scale. However, the interaction between urban growth and resident welfare may touch upon multiple dimensions of welfare coincidently, and it may take different forms across geographical scales. This thesis focuses on urban growth in China, against the background of a newly industrializing and developing country. Through the housing market dynamic, its central claim is to explore the tensions between the positive and negative effects of urban growth on resident welfare at multiple dimensions (economic, social, environmental, and policy) and scales (intra-city, inter-city, and countrywide) from a spatial inequality perspective

Predicting Multi-level Socioeconomic Indicators from Structural Urban Imagery

Funding Information: This research has been supported in part by the National Key Research and Development Program of China under Grant 2020YFB2104005; in part by the National Natural Science Foundation of China under Grant U20B2060, and Grant U21B2036; in part by the International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program) under YJ20210274; and in part by the Academy of Finland under Project 319669, Project 319670, Project 325570, Project 326305, Project 325774, and Project 335934. Publisher Copyright: © 2022 Owner/Author.Understanding economic development and designing government policies requires accurate and timely measurements of socioeconomic activities. In this paper, we show how to leverage city structural information and urban imagery like satellite images and street view images to accurately predict multi-level socioeconomic indicators. Our framework consists of four steps. First, we extract structural information from cities by transforming real-world street networks into city graphs (GeoStruct). Second, we design a contrastive learning-based model to refine urban image features by looking at geographic similarity between images, with images that are geographically close together having similar features (GeoCLR). Third, we propose using street segments as containers to adaptively fuse the features of multi-view urban images, including satellite images and street view images (GeoFuse). Finally, given the city graph with a street segment as a node and a neighborhood area as a subgraph, we jointly model street- and neighborhood-level socioeconomic indicator predictions as node and subgraph classification tasks. The novelty of our method is that we introduce city structure to organize multi-view urban images and model the relationships between socioeconomic indicators at different levels. We evaluate our framework on the basis of real-world datasets collected in multiple cities. Our proposed framework improves performance by over 10% when compared to state-of-the-art baselines in terms of prediction accuracy and recall.Peer reviewe

Variations in the Spatial Distribution of Smart Parcel Lockers in the Central Metropolitan Region of Tianjin, China: A Comparative Analysis before and after COVID-19

The COVID-19 pandemic has led to a significant increase in e-commerce, which has prompted residents to shift their purchasing habits from offline to online. As a result, Smart Parcel Lockers (SPLs) have emerged as an accessible end-to-end delivery service that fits into the pandemic strategy of maintaining social distance and no-contact protocols. Although numerous studies have examined SPLs from various perspectives, few have analyzed their spatial distribution from an urban planning perspective, which could enhance the development of other disciplines in this field. To address this gap, we investigate the distribution of SPLs in Tianjin’s central urban area before and after the pandemic (i.e., 2019 and 2022) using kernel density estimation, average nearest neighbor analysis, standard deviation elliptic, and geographical detector. Our results show that, in three years, the number of SPLs has increased from 51 to 479, and a majority were installed in residential communities (i.e., 92.2% in 2019, and 97.7% in 2022). We find that SPLs were distributed randomly before the pandemic, but after the pandemic, SPLs agglomerated and followed Tianjin’s development pattern. We identify eight influential factors on the spatial distribution of SPLs and discuss their individual and compound effects. Our discussion highlights potential spatial distribution analysis, such as dynamic layout planning, to improve the allocation of SPLs in city planning and city logistics

Localização varejista : um estudo sobre a configuração espacial urbana e as atividades varejistas em Santa Maria - RS

O comércio varejista é um componente fundamental na vida diária nas cidades contemporâneas e o estudo acerca da lógica de localização desta atividade é essencial para se compreender e planejar espaços urbanos. A distribuição do comércio varejista é influenciada por diferentes fatores, apresentando-se em padrões espaciais complexos e dinâmicos. Dentre os fatores que afetam a localização varejista, ressalta-se o tipo de varejo, a distribuição dos consumidores e a acessibilidade entre ambos, condicionada pela morfologia urbana. O presente trabalho tem como foco a análise da relação entre o padrão locacional varejista e a configuração urbana, tema ainda pouco explorado no contexto brasileiro. O objetivo geral da pesquisa é avaliar a influência de diferentes atributos da configuração espacial urbana na localização varejista, com enfoque a um caso brasileiro. Pretende-se responder às seguintes questões: Em que medida a proximidade aos consumidores ou a outros varejistas está associada à localização desta atividade? Em que medida a exposição das localizações ao movimento dos consumidores associa-se à presença do varejo? E, quais desses fatores (proximidade e movimento) são mais influentes para a decisão locacional varejista nas cidades? O trabalho é aplicado à cidade de Santa Maria - RS e a metodologia baseia-se em métricas configuracionais a partir de três fatores relevantes para a escolha locacional varejista: a aglomeração dos estabelecimentos, a proximidade à população consumidora e o movimento das pessoas na cidade. Para se testar a significância estatística dessas diferentes métricas nas escolhas de localização do varejo, adota-se a metodologia de interação estratégica da econometria espacial aplicada a estudos de localização, por meio de técnicas de associação categórica e de regressão múltipla. O trabalho demonstra que as escolhas de localização dos estabelecimentos varejistas diferem entre si dependendo do tipo de mercadoria ofertada. Além de concluir que a tomada de decisão locacional de agentes varejistas vincula-se em maior grau aos espaços com maior potencial de intermediação do que em relação aos espaços mais acessíveis, os resultados destacam a importância de se analisar as contribuições desagregadas dos movimentos de pessoas na cidade.Retail trade is a fundamental component of daily life in contemporary cities, and studying the logic of its location is essential for understanding and planning urban spaces. The distribution of retail trade is influenced by different factors, presenting complex and dynamic spatial patterns. Among the factors that affect retail location, the type of retail, distribution of consumers, and accessibility between them, conditioned by urban morphology are highlighted. This study focuses on analyzing the relationship between the retail locational pattern and urban configuration, a topic that is still little explored in the Brazilian context. The general objective of the research is to evaluate the influence of different attributes of urban spatial configuration on retail location, focusing on a Brazilian case. The following questions are intended to be answered: To what extent is proximity to consumers or other retailers associated with the location of this activity? To what extent is the exposure of locations to consumer movement associated with the presence of retail? And which of these factors (proximity and movement) are most influential in retail locational decision-making in cities? The study is applied to the city of Santa Maria - RS and the research methodology is based on configurational metrics based on three relevant factors for retail locational choice: the agglomeration of establishments, proximity to the consuming population, and the people movement in the city. In order to test the statistical significance of these different metrics on retail location choices, the strategic interaction methodology from spatial econometrics applied to location studies is adopted, using categorical association and multiple regression techniques. The study reveals that retail establishments' location choices vary contingent upon the type of merchandise offered. Moreover, the findings suggest that retail agents' locational decision-making is more strongly correlate The study reveals that retail establishments' location choices vary contingent upon the type of merchandise offered. Moreover, the findings suggest that retail agents' locational decision-making is more strongly correlated to areas exhibiting higher potential for traffic than with spaces that are more easily accessible. The results underscore the significance of examining the disaggregated impacts of people's movements within the city

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

Modeling Urban Venue Dynamics through Spatio-Temporal Metrics and Complex Networks

The ubiquity of GPS-enabled devices, mobile applications, and intelligent transportation systems have enabled opportunities to model the world at an unprecedented scale. Urban environments, in particular, have benefited from new data sources that provide granular representations of activities across space and time. As cities experienced a rise in urbanization, they also faced challenges in managing vehicle levels, congestion, and public transportation systems. Modeling these fast-paced changes through rich data from sources such as taxis, bikes, and trains has enabled prediction models capable of characterizing trends and forecasting future changes. Data-driven studies of urban mobility dynamics have been instrumental in helping deliver more contextual services to cities, support urban policy, and inform business decisions. This dissertation explores how novel algorithmic architectures and techniques reveal and predict business trends and urban development patterns. The research informing this dissertation harnesses principles from network science, modeling cities as connected networks of venues. Building upon a foundation of research in complex network theory, urban computing, and machine learning, we propose algorithms tailored for three computing tasks focused on modeling venue dynamics, characteristics, and trends. First, we predict the demand for newly opened businesses using insights from movement patterns across different regions of the city. Through this analysis we demonstrate how temporally similar areas can be successfully used as inputs to predict the visitation patterns of new venues. Next, we forecast the likelihood of business failure through a supervised learning model. We analyze the value of varying features in predicting business failure and explore their impact across new and established venues and across different cities worldwide. Finally, we present a deep learning architecture which integrates both spatial and topological features to predict the future demand for a venue. These works highlight the power of complex network measures to quantify the structure of a city and inform prediction models. This dissertation leverages vast amounts of data from spatio-temporal networks to model venue dynamics. The research puts forward evidence to support a data-driven study of geographic systems applied to fundamental questions in urban studies, retail development, and social science.Gates Cambridge Trus

GIS in Healthcare

The landscape of healthcare is dynamic, gradually becoming more complicated with factors beyond simple supply and demand. Similar to the diversity of social, political and economic contexts, the practical utilization of healthcare resources also varies around the world. However, the spatial components of these contexts, along with aspects of supply and demand, can reveal a common theme among these factors. This book presents advancements in GIS applications that reveal the complexity of and solutions for a dynamic healthcare landscape

Urban and Regional Cooperation and Development

This is an open access book. This book, first of all, introduces the new unveiled Guangdong-Macao Intensive Cooperation Zone with details as a special mode of the regional collaborative development that is committed to be mutually beneficial to both sides with different political and economic systems. China's central authorities have recently issued a masterplan for constructing the Guangdong-Macao Intensive Cooperation Zone at Hengqin Island in September 2021. As China's first and last European colony and one of China’s two special administrative regions (SARs), Macao has developed the gambling industry seven times larger than that of Las Vegas. However, the problem of the homogeneous industrial structure and the urgent need to promote sustainable economic growth by regional cooperation have been important theoretical and practical issues discussed by scholars and policy-makers. The Guangdong-Macao Intensive Cooperation Zone (ICZ) is managed under special customs supervision between two boarder lines and expected to diversify Macao’s economy. Then, this book dissects the theory of regional synergistic development and its applications in a number of international comparative and cross-interdisciplinary case studies worldwide. Finally, from the perspective of land use, transportation connection, and social service, this book thoroughly explores the challenges and strategies to implement the new cooperation model within the framework of one country, two systems, two customs, and two currencies to achieve a win–win situation using updated first-hand data collected by literature review, case study, field survey, spatial analysis, and interview

Geo Data Science for Tourism

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.