Xiaodan Su

Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations

Urban cellular automata (CA) models are broadly used in quantitative analyses and predictions of urban land-use dynamics. However, most urban CA developed with neighborhood rules consider only a small neighborhood scope under a specific spatial resolution. Here, we quantify neighborhood effects in a relatively large cellular space and analyze their role in the performance of an urban land use model. The extracted neighborhood rules were integrated into a commonly used logistic regression urban CA model (Logistic-CA), resulting in a large neighborhood urban land use model (Logistic-LNCA). Land-use simulations with both models were evaluated with urban expansion data in Xiamen City, China. Simulations with the Logistic-LNCA model raised the accuracies of built-up land by 3.0%-3.9% in two simulation periods compared with the Logistic-CA model with a 3?×?3 kernel. Parameter sensitivity analysis indicated that there was an optimal large window size in cellular space and a corresponding optimal parameter configuration. Extended neighborhood effects and their influence on urban dynamics were addressed in this study.A logistic regression urban CA model incorporating the extracted neighborhood rules, Logistic-LNCA, was developed.The Logistic-LNCA model achieved higher simulation accuracy than the Logistic-CA model with a 3?×?3 kernel.The simulation accuracy and the Kappa coefficient varied with window sizes and radius intervals.There is an optimal window size in cellular space and corresponding optimal parameter configurations.

A neighbor decay cellular automata approach for simulating urban expansion based on particle swarm intelligence

Simulation and quantitative analysis of urban land use change are effective ways to investigate urban form evolution. Cellular Automata (CA) has been used as a convenient and useful tool for simulating urban land use change. However, the key issue for CA models is the definition of the transition rules, and a number of statistical or artificial intelligence methods may be used to obtain the optimal rules. Neighborhood configuration is a basic component of transition rules, and is characterized by a distance decay effect. However, many CA models do not consider the neighbor decay effect in cellular space. This paper presents a neighbor decay cellular automata model based on particle swarm optimization (PSO-NDCA). We used particle swarm optimization (PSO) to find transition rules and considered the decay effect of the cellular neighborhood. A negative power exponential function was used to compute the decay coefficient of the cellular neighborhood in the model. By calculating the cumulative differences between simulation results and the sample data, the PSO automatically searched for the optimal combination of parameters of the transition rules. Using Xiamen City as a case study, we simulated urban land use changes for the periods 1992–1997 and 2002–2007. Results showed that the PSO-NDCA model had a higher prediction accuracy for built-up land, and a higher overall accuracy and Kappa coefficient than the urban CA model based on particle swarm optimization. The study demonstrates that there exist optimal neighborhood decay coefficients in accordance with the regional characteristics of an area. Urban CA modelling should take into account the role of neighborhood decay.

A Clustering-Assisted Regression (CAR) approach for developing spatial climate data sets in China

There is an increasing demand for improving spatial resolution of climate data. However, an increase in resolution does not necessarily mean an increase in realism and accuracy if local spatial features, such as elevational effects, cannot be considered in developing higher-resolution climate data. The Gradient plus Inverse Distance Squared (GIDS) is a broadly accepted elevation-dependent method for spatial interpolation of climate data but is relatively less effective in predicting climate variables in mountainous regions. We developed a new method called Clustering-Assisted Regression (CAR). Instead of using a fixed number of neighboring observed stations within a moving window, we repeated cluster analysis to derive a new subset of stations for each estimated site in CAR. We used both GIDS and CAR to estimate monthly mean temperature and monthly precipitation across mainland China based on observation data from 719 national meteorological stations. Both GIDS and CAR interpolation methods behaved reasonably well in developing 1 km resolution spatial data of monthly mean temperature and monthly precipitation at a national scale in mainland China. The accuracy of monthly mean temperature in summer was higher than in winter whereas that of monthly precipitation in winter was better than in summer. Overall comparisons indicate that CAR was slightly more accurate than GIDS, especially for predicting local climate patterns.

Effects of spatial form on urban commute for major cities in China

The global phenomenon of urbanization increases the importance of compact-city development. China’s rapid urban development has resulted in unprecedented urban population growth and built-up area expansion, but its effects on urban morphology and mobility are only partly understood. City compactness can be measured simply using urban spatial form or morphology: the more concentrated the built-up area, the more compact the city is. Here we show that 35 major cities in China are not compact in spatial form and that their compactness is not improving over time. Our results reveal close correlations between changes in urbanization rate and changes in city compactness as well as between city compactness and commuting time (CT), indicating that the high rate of urbanization without adequate planning has contributed to the poor compactness of Chinese cities, which has further increased CT. We suggest that continuing urban sprawl with low land use efficiency and low urban form compactness will make cities in China more congested and threaten China’s sustainable urbanization.

An integrated system for urban environmental monitoring and management based on the Environmental Internet of Things

There have been a growing number of environmental problems associated with the rapid development of cities. Common environmental monitoring methods are unable to meet the dynamic needs of urban environmental management. The emergence of Internet of Things (IoT) technology provides a new way to improve urban environment monitoring and management. The Environmental Internet of Things (EIoT) makes it possible to sense, acquire, process, and transfer environmental information over a large area in real time. In this paper, we present an integrated system for urban environment monitoring and management by referring to the EIoT concept. We developed and tested the system by monitoring water, soil, air, noise, and some other environmental factors on the campus of our research institute. The system can obtain real-time environmental information in situ and express and publish its outcomes in different formats. Moreover, the system is extendible for additional sensors and environmental factors, and to cover larger areas to achieve better urban environmental monitoring and management services for the construction of sustainable cities.

Experimental mobile environmental monitoring and real-time analysis as an initial application of EIoT in town villages in China

Villages in China have been greatly impacted by the countrywide urbanisation process, and many of them have undergone transformation in landscape structure and rapid changes in environment. However, environmental monitoring in urbanising villages, or town villages, cannot be fully realised by the existing environmental monitoring infrastructure and resources. We intended to apply Environmental Internet of Things (EIoT) technology to fulfill this task, beginning with the development of a Mobile Meteorological Monitor (3M) consisting of an ultrasonic weather station, an industrial tablet PC, a foldable mountain bicycle and a series of accessories. The initial application of 3M in environmental monitoring for a town village indicates that the monitoring capability could be expanded to carry out the monitoring of other environmental variables, such as water quality, air pollution and noise pollution. By simultaneously employing multiple 3M units, it is possible to generate a spatiotemporal framework of monitoring data for landscape-level environmental analysis and modelling. Such an approach is economically affordable and scientifically reliable. The application of EIoT as it matures over time will revolutionise future environmental monitoring and governance.

Design of an EIoT system for nature reserves: a case study in Shangri-La County, Yunnan Province, China

The technology of Environmental Internet of Things (EIoT) has various advantages over conventional field data collection methods for better understanding of how nature reserves are protected and managed. This is mainly because EIoT systems can help collect a vast amount of real-time data, from which rich and dynamic information can be obtained for comprehensive analysis of spatial pattern and processes of key elements of nature resources, facilitating the sustainable management of nature reserves. However, there are practical considerations in the installation and maintenance of EIoT systems because of harsh environment and remote locations of many nature reserves. We did a preliminary EIoT experiment in Shangri-La County, Yunnan Province, China, based on which we proposed a technically simple solution for researchers to custom suitable EIoT instruments in nature reserves. We also put forward a few methods to calculate key parameters of power supply units and system availability. This EIoT system is configured for applications in nature reserves similar to that in Shangri-La County though further tests are necessary.

Landsenses ecological planning for the Xianghe Segment of China’s Grand Canal

ABSTRACT Traditional methods of urban planning mainly focus on urban land, population size and transport priorities, and fail to consider environmental quality and welfare. To build a suitable urban form for future residents, urban eco-planners should consider the various data and technologies available, especially a resident’s sensitivity in their planning. Rational and scientific planning needs comprehensive analysis of urban ecosystems, including both natural and human factors. The new concept and theory, ‘landsenses’, can help urban planners to integrate human sensitivity into their blueprint. Excellent eco-planning should include all forms of human senses, that is sight, hearing, taste, smell and touch. Based on the framework of landsenses ecology, we tentatively made a landsenses ecological planning for the Xianghe Segment of China’s Grand Canal (XSCGC), in which we addressed the importance and challenges to incorporate a resident’s sensory information into the ecological planning process, to promote the use of mix-marching data and the Internet of Things.

Redefining the digital city for promoting sustainable urban development

In the current information age, digital technology has become an essential part of urban civilisation. The digital city has been transformed from a novel concept to a practical and effective means of supporting urban planning and management. However, there are various definitions of a digital city and each has a unique significance. By comparing these digital city concepts, we examined common aspects of digital city definitions and propose an urban digital operating system (Urban DOS) that will be useful to improve life quality, socioeconomic functions and sustainable development in a city and its surrounding areas. The technical basis for the Urban DOS is the intersection between technology-oriented products (TOPs) and customised application products (CAPs). We then develop a procedure for designing a framework for a digital city based on Urban DOS with TOPs and CAPs. To explain such digital city concepts and applications, we demonstrate the initial development of Urban DOS for Lijiang City.

Risk assessment for effective prevention and management of forest fires in Lijiang City

Forest fires threaten natural resources and human lives in many areas of the world. A rational assessment of forest fire risk is critical to reduce fire damage that threatens the sustainability of forest resources and their services. This is particularly true in Lijiang City, an important world heritage site. We assessed the grades of forest fire risk in Lijiang City based on the concept of a fire life cycle, using the probability of ignition in the pre-forest fire period, the capacity for detection and emergency rescue in the mid-forest fire period, and forest fire damage in the post-forest fire period. We used the analytical hierarchy process to analyse data on the ecology, economy, cultural resources, humanities and topography of Lijiang City, and geographic information systems (GIS) as a platform to integrate multi-source data. The results strongly agree with the records of reported forest fires between 2000 and 2011. This assessment method could be used in cities with large areas forestland that contains important resources and settlements, but without sufficient fire-fighting capacity to prevent and fight forest fires.