Microsimulation

From origins in economics and financial analysis, microsimulation has become an important technique for spatial analysis. The method relies on conversion of aggregate census tables, sometimes complemented by sample data at the individual level, to synthetic lists of people and households. The individual records generated by the microsimulation can be aggregated flexibly to small areas, linked to create new attributes, and projected forward in time under stable conditions, or in the context of ‘what-if’ policy scenarios. The chapter outlines the basic building blocks of microsimulation and shows how these are combined within a representative practical application. It is argued that further progress can be expected through advances in computation, assimilation of data into models, and greater capacity to handle uncertainty and dynamics. We also expect the creation of more sophisticated architectures to reflect the interdependence between population structures at the micro-scale, and the supply-side infrastructures and urban environments in which they evolve.

Spatial Economics, Urban Informatics, and Transport Accessibility

One central pillar in the development of urban science which is key to the development of simulation of models of urban structure is spatial econometrics. In this chapter, we outline the way in which ideas pertaining to accessibility which we define conventionally, as in transport economics, as the relative nearness and size of locations to one another, can be embedded in a wider econometric framework. We are thus able to explore how GDP (gross domestic product) of different locations is influenced by different spatial investments. To illustrate this, we first outline the intellectual context, followed by a review of the most relevant econometric models. We examine the data required for such models and look at various quantifications in terms of elasticities of business productivity with respect to transport accessibility, using ordinary least squares, time-series fixed effects, and a range of dynamic panel-data models which narrow down the valid range of estimates. We then show how the model is applied to Guangdong province (with its connections to Hong Kong and Macau), which is one of the three major mega-city regions and a leading adopter of new technologies in China.

Urban Crime and Security

Scientists have an enduring interest in understanding urban crime and developing security strategies for mitigating this problem. This chapter reviews the progress made in this topic from historic criminology to data-driven policing. It first reviews the broad implications of urban security and its implementation in practice. Next, it focuses on the tools to prevent urban crime and improve security, from analytical crime hotspot mapping to police resource allocation. Finally, a manifesto of data-driven policing is proposed, with its practical demand for efficient security strategies and the development of big data technologies. It emphasizes that data-driven strategies could be applied in cities due to their promising effectiveness for crime prevention and security improvement.

Urban Risks and Resilience

The resilience concept has become more significant in the past decade as a means for understanding how cities prepare and plan for, absorb, recover from, and more successfully adapt to adverse events. Definitional differences—resilience as an outcome or end-point versus resilience as a process of building capacity—dominate the literature. Lagging behind are efforts to systematically measure resilience to produce a baseline and subsequent monitoring, in order to gauge what, where, and how intervention or mitigation strategies would strengthen or weaken urban resilience. The chapter reviews research and practitioner attempts to develop urban informatics for resilience and provides selected case studies of cities as exemplars.

Optical Remote Sensing

Applications of Earth-observational remote sensing are rapidly increasing over urban areas. The latest regime shift from conventional urban development to smart-city development has triggered a rise in smart innovative technologies to complement spatial and temporal information in new urban design models. Remote sensing-based Earth-observations provide critical information to close the gaps between real and virtual models of urban developments. Remote sensing, itself, has rapidly evolved since the launch of the first Earth-observation satellite, Landsat, in 1972. Technological advancements over the years have gradually improved the ground resolution of satellite images, from 80 m in the 1970s to 0.3 m in the 2020s. Apart from the ground resolution, improvements have been made in many other aspects of satellite remote sensing. Also, the method and techniques of information extraction have advanced. However, to understand the latest developments and scope of information extraction, it is important to understand background information and major techniques of image processing. This chapter briefly describes the history of optical remote sensing, the basic operation of satellite image processing, advanced methods of object extraction for modern urban designs, various applications of remote sensing in urban or peri-urban settings, and future satellite missions and directions of urban remote sensing.

Defining Urban Science

This introductory chapter provides a brief overview of the theories and models that constitute what has come to be called urban science. Explaining and measuring the spatial structure of the city in terms of its form and function is one of the main goals of this science. It provides links between the way various theories about how the city is formed, in terms of its economy and social structure, and how these theories might be transformed into models that constitute the operational tools of urban informatics. First the idea of the city as a system is introduced, and then various models pertaining to the forces that determine what is located where in the city are presented. How these activities are linked to one another through flows and networks are then introduced. These models relate to formal models of spatial interaction, the distribution of the sizes of different cities, and the qualitative changes that take place as cities grow and evolve to different levels. Scaling is one of the major themes uniting these different elements grounding this science within the emerging field of complexity. We then illustrate how we might translate these ideas into operational models which are at the cutting edge of the new tools that are being developed in urban informatics, and which are elaborated in various chapters dealing with modeling and mobility throughout this book.

Overall Introduction

Urban informatics is an interdisciplinary approach to understanding, managing, and designing the city using systematic theories and methods based on new information technologies. Integrating urban science, geomatics, and informatics, urban informatics is a particularly timely way of fusing many interdisciplinary perspectives in studying city systems. This edited book aims to meet the urgent need for works that systematically introduce the principles and technologies of urban informatics. The book gathers over 40 world-leading research teams from a wide range of disciplines, who provide comprehensive reviews of the state of the art and the latest research achievements in their various areas of urban informatics. The book is organized into six parts, respectively covering the conceptual and theoretical basis of urban informatics, urban systems and applications, urban sensing, urban big data infrastructure, urban computing, and prospects for the future of urban informatics. This introductory chapter provides a definition of urban informatics and an outline of the book’s structure and scope.

Introduction to Urban Sensing

This chapter overviews the urban sensing technologies for unban informatics to be introduced in the subsequent chapters under Part III of this book. To be covered is a wide range of technologies for urban sensing from the space, the air, the ground, the underground, and on individuals, including optical remote sensing, interferometric synthetic aperture radar, light detection and ranging, photogrammetry, underground sensing, mobile mapping, indoor positioning, ambient sensing, and the use of user-generated content.

User-Generated Content: A Promising Data Source for Urban Informatics

This chapter summarizes different types of user-generated content (UGC) in urban informatics and then gives a systematic review of their data sources, methodologies, and applications. Case studies in three genres are interpreted to demonstrate the effectiveness of UGC. First, we use geotagged social media data, a type of single-sourced UGC, to extract citizen demographics, mobility patterns, and place semantics associated with various urban functional regions. Second, we bridge UGC and professional-generated content (PGC), in order to take advantage of both sides. The third application links multi-sourced UGC to uncover urban spatial structures and human dynamics. We suggest that UGC data contain rich information in diverse aspects. In addition, analysis of sentiment from geotagged texts and photos, along with the state-of-the-art artificial intelligence methods, is discussed to help understand the linkage between human emotions and surrounding environments. Drawing on the analyses, we summarize a number of future research areas that call for attention in urban informatics.

Mobile Mapping Technologies

This chapter introduces the historic development as well as the latest progress of mobile mapping systems. First, mobile mapping technologies, including the introduction of positioning and mapping sensors, and how they can be integrated together, are briefly reviewed. Then the development of land-based, aerial, marine, and mobile portable mapping platforms is presented. The latest progress in mobile-mapping technologies is further discussed, along with sensor fusion schemes, seamless indoor and outdoor mapping strategies, and disaster response applications. In addition, this chapter explores future and potential applications, such as high-definition (HD) maps and autonomous mapping with autonomous systems.