Network optimization for enhanced resilience of urban heat island measurements

The urban heat island is a well-known phenomenon that impacts a wide variety of city operations. With greater availability of cheap meteorological sensors, it is possible to measure the spatial patterns of urban atmospheric characteristics with greater resolution. To develop robust and resilient networks, recognizing sensors may malfunction, it is important to know when measurement points are providing additional information and also the minimum number of sensors needed to provide spatial information for particular applications. Here we consider the example of temperature data, and the urban heat island, through analysis of a network of sensors in the Tokyo metropolitan area (Extended METROS). The effect of reducing observation points from an existing meteorological measurement network is considered, using random sampling and sampling with clustering. The results indicated the sampling with hierarchical clustering can yield similar temperature patterns with up to a 30% reduction in measurement sites in Tokyo. The methods presented have broader utility in evaluating the robustness and resilience of existing urban temperature networks and in how networks can be enhanced by new mobile and open data sources

Intra firm and extra firm networks in the German knowledge economy. Economic development of German agglomerations from a relational perspective

Flows and inter-linkages between and within polycentric metropolitan regions have become a fundamental topic in regional sciences. The knowledge economy as a primary driver of spatial restructuring is forming these relations by generating knowledge within a spatially fine graded division of labor. This process drives companies to cooperate in intra firm and extra firm networks which in turn evoke patterns of interdependent spatial entities. The aim of the paper is twofold. Firstly, we analyze spatial patterns within these firm networks and secondly we combine this network approach with the development of the economic and spatial structure of German agglomerations. Inspired by formal social network analysis and spatial association statistics we apply methods to discover spatial clustering within relational data. We assume that relations between and within polycentric Mega-City Regions in Germany and its neighboring areas constitute a new form of hierarchical urban systems. Network analysis will help to detect locations of high centrality; cluster analyses of location-based data may show specific regional patterns of connectivity. We hypothesize that the position of locations within the functional urban hierarchy depends on the spatial scale of analysis: global, European, national or regional. Furthermore, we combine this relational perspective with an analysis of the economic development within these spatial entities. Here we assume that intensive interaction between functional urban areas has a high influence on their performance over time with regard to output indicators like labor, value-added and gross domestic product. Therefore we apply methods of spatial and network autocorrelation. We hypothesize that relational proximity influences economic development more intensively than effects of agglomeration and geographical proximity do.

Predictive Inference for Spatio-temporal Precipitation Data and Its Extremes

Modelling of precipitation and its extremes is important for urban and agriculture planning purposes. We present a method for producing spatial predictions and measures of uncertainty for spatio-temporal data that is heavy-tailed and subject to substaintial skewness which often arise in measurements of many environmental processes, and we apply the method to precipitation data in south-west Western Australia. A generalised hyperbolic Bayesian hierarchical model is constructed for the intensity, frequency and duration of daily precipitation, including the extremes. Unlike models based on extreme value theory, which only model maxima of finite-sized blocks or exceedances above a large threshold, the proposed model uses all the data available efficiently, and hence not only fits the extremes but also models the entire rainfall distribution. It captures spatial and temporal clustering, as well as spatially and temporally varying volatility and skewness. The model assumes that the regional precipitation is driven by a latent process characterised by geographical and climatological covariates. Effects not fully described by the covariates are captured by spatial and temporal structure in the hierarchies. Inference is provided by MCMC using a Metropolis-Hastings algorithm and spatial interpolation method, which provide a natural approach for estimating uncertainty. Similarly both spatial and temporal predictions with uncertainty can be produced with the model.Comment: Under review at Journal of the American Statistical Association. 27 pages, 10 figure

A Probabilistic Embedding Clustering Method for Urban Structure Detection

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by learning via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.Comment: 6 pages, 7 figures, ICSDM201

Decoding the urban grid: or why cities are neither trees nor perfect grids

In a previous paper (Figueiredo and Amorim, 2005), we introduced the continuity lines, a compressed description that encapsulates topological and geometrical properties of urban grids. In this paper, we applied this technique to a large database of maps that included cities of 22 countries. We explore how this representation encodes into networks universal features of urban grids and, at the same time, retrieves differences that reflect classes of cities. Then, we propose an emergent taxonomy for urban grids