An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago

The sharp rise in urban crime rates is becoming one of the most important issues of public security, affecting many aspects of social sustainability, such as employment, livelihood, health care, and education. Therefore, it is critical to develop a predictive model capable of identifying areas with high crime intensity and detecting trends of crime occurrence in such areas for the allocation of scarce resources and investment in the prevention and reduction of criminal strategies. This study develops a predictive model based on K-means clustering, signal decomposition technique, and neural networks to identify crime distribution in urban areas and accurately forecast the variation tendency of the number of crimes in each area. We find that the time series of the number of crimes in different areas show a correlation in the long term, but this long-term effect cannot be reflected in the short period. Therefore, we argue that short-term joint law enforcement has no theoretical basis because data show that spatial heterogeneity and time lag cannot be timely reflected in short-term prediction. By combining the temporal and spatial effects, a high-precision anticrime information support system is designed, which can help the police to implement more targeted crime prevention strategies at the micro level.     

Has Dongying developed to a ghost city? - Evidence from multi-temporal population estimation based on VHR remote sensing and census counts

With ongoing growth and continuous development of cities, the world is turning into an urban society. In this context, urbanization, population growth and migration towards urban areas are global trends. These processes are highly dynamic especially in China, with highest rates of urbanization worldwide. In contrast to these well-known trends, a recently emerging and rarely studied side effect is “ghost cities”, which became a notable phenomenon in China recently. A ghost city is commonly defined as a new urban development that is running at severe undercapacity with respect to population and businesses and where the availability of housing and public infrastructure significantly exceeds the practical demand. Against this background, this study presents a framework based on remote sensing for the assessment of the presence or absence of the ghost city phenomenon in a typical highly dynamic Chinese city. For this purpose, remote sensing data with very-high resolution (VHR) are employed for establishment of a 4d functional city model. Subsequently population capacity estimates are based on a statistical approach. The components of the functional 4d city model, i.e. the multi-temporal building model and the classification of building types associated with residential and non-residential function returned very high accuracies with κ of 0.73 and 0.89, respectively. The number of floors was estimated with coefficient of determination of 0.91. Compared to the numbers of official census counts, the multi-temporal population capacity estimation revealed a considerable mismatch of available living space based on VHR remote sensing data and actual population counts. According to the conceptual framework of this study, this disagreement indicates a high likelihood and significant evidence for the emergence and presence of the ghost city phenomenon for the city of Dongying. In addition, a detailed spatial assessment was conducted in terms of an index comparing the dynamics of residential developments and population numbers to provide an impression of specific regions of the urban area which are most likely to suffer from the ghost city phenomenon.     

基于MEMD和空间层次聚类的PM2.5三角模糊序列多因子组合预测

日间PM2.5浓度受本地和邻近地区的多重因素影响,具有高度不确定性和不稳定性.常见的PM2.5实值序列和区间序列分别反映其日均和极值波动状况,三角模糊序列将两者优点相结合可包含更多的有效信息.基于此,提出基于多元经验模态分解(multiple empirical mode decomposition,MEMD)和空间层次聚类的PM2.5三角模糊序列多因子组合预测模型.首先,运用皮尔曼相关系数分析PM2.5与本地污染物浓度、气象要素间的关联度,选取本地影响因子;其次,计算PM2.5与空间污染物浓度间的关联度,并据此对邻近城市K-means空间聚类得到核心影响、一般影响和偏远影响城市群,并统计各城市群不同污染物的综合指数,即空间影响因子;进而,利用MEMD对PM2.5和影响因子的三角模糊序列同时进行分解,重构得到高频、低频以及趋势序列;最后,运用BP神经网络、长短记忆神经网络(long short-term memory,LSTM)、最小二乘支持向量回归(least squares support vector regression, LSSVR)分别对子序列进行多输入单输出的预测,并将上述单项预测结果相加,即得到PM2.5三角模糊序列的预测值.仿真实验结果表明,所提出的模型能够充分考虑气象条件和多种污染物的空间影响,具有较强的预测精度和良好的实用性.     

Measuring urban regional similarity through mobility signatures

The task of identifying similar regions within and between cities is an important aspect of urban data science as well as applied domains such as real estate, tourism, and urban planning. Regional similarity is typically assessed through comparing socio-demographic variables, resource availability, or urban infrastructure. An essential dimension, often overlooked for this task, is the spatiotemporal mobility patterns of people within a city. In this work we present a novel approach to identifying regional similarity based on human mobility as proxied through micromobility trips. We use a dataset consisting of e-scooter trip origins and destinations for two major European cities that differ in population size and urban structure. Three dimensions of these data are used in modeling the spatial and temporal variability in movement between regions in cities, allowing us to compare regions through a mobility lens. The result is a parameterized similarity model and interactive web platform for comparing regions across different urban environments. The application of this model suggests that human mobility patterns are a quantifiable, unique, and appropriate characteristic through which to measure urban similarity.     

Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events

The predictive hotspot mapping of sparse spatio-temporal events (e.g., crime and traffic accidents) aims to forecast areas or locations with higher average risk of event occurrence, which is important to offer insight for preventative strategies. Although a network-based structure can better capture the micro-level variation of spatio-temporal events, existing deep learning methods of sparse events forecasting are either based on area or grid units due to the data sparsity in both space and time, and the complex network topology. To overcome these challenges, this paper develops the first deep learning (DL) model for network-based predictive mapping of sparse spatio-temporal events. Leveraging a graph-based representation of the network-structured data, a gated localised diffusion network (GLDNet) is introduced, which integrating a gated network to model the temporal propagation and a novel localised diffusion network to model the spatial propagation confined by the network topology. To deal with the sparsity issue, we reformulate the research problem as an imbalance regression task and employ a weighted loss function to train the DL model. The framework is validated on a crime forecasting case of South Chicago, USA, which outperforms the state-of-the-art benchmark by 12% and 25% in terms of the mean hit rate at 10% and 20% coverage level, respectively.     

基于遥感和微博签到数据的住房空置空间识别

房屋空置率是衡量房地产市场健康与否的重要指标。基于夜间灯光遥感数据和全国土地覆盖数据,对房屋空置率进行空间识别。利用微博签到数据,通过基于密度的聚类算法和热力分析对居民活动空间强度进行分析,从“鬼城”指数排名靠前的100个城市中随机选择30个样本城市,进行城市内部房屋空置空间识别。结果表明：2013年全国地级及以上城市基于像元的平均房屋空置率为27.3%。东部地区房屋空置率较低,中西部地区房屋空置现象明显;房屋空置在中小型城市更加突出。     

Study on the Spatial Identification of Housing Vacancy

Housing Vacancy Rate (HVR) is an important index in assessing the healthiness of residential real estate market. Due to lack of clear and effectively evaluation criterion, the understanding of housing vacancy in China is then rather limited. This paper quantitatively analyzed spatial identification and difference pattern of house vacancy at different scale in China by using nighttime light data and micro-blog check-in data, in order to make up the deficiency of traditional methods in the aspects of data missing and differential approach. The nighttime light intensity for non-vacancy area is estimated after removing the nighttime light intensity from non-residential sources of NPP-VIIRS light data and difference of nighttime light caused by the different urban area ratio. Then, the HVR is calculated for the spatial pattern analysis. This paper analyzed the spatial strength of residents activities by using micro-blog check-in data, based on density-based spatial clustering of applications with noise and heat map. The 30 sample cities were selected to identify house vacancy from 100 cities which ghost city index were high. The following conclusions were drawn through the study: The HVR of eastern coastal cities and regions with rapid development of economy were lower, while the phenomenon of house vacancy in central and western regions were more obvious. The HVR increased from eastern coastal regions to inland areas. What’s more, the phenomenon of house vacancy in middle and small cities were more distinct from the aspect of urban scale. The house vacancy of China were divided into five types: industry or resources driven, government planned, epitaxy expansionary, environmental constraint and speculative activate by taking the factors of natural environment, social economic development level, and population density into consideration. This may shed light on policy implications for Chinese urban development.     

What is wrong about Robocops as consultants? A technology-centric critique of predictive policing

Fighting crime has historically been a field that drives technological innovation, and it can serve as an example of different governance styles in societies. Predictive policing is one of the recent innovations that covers technical trends such as machine learning, preventive crime fighting strategies, and actual policing in cities. However, it seems that a combination of exaggerated hopes produced by technology evangelists, media hype, and ignorance of the actual problems of the technology may have (over-)boosted sales of software that supports policing by predicting offenders and crime areas. In this paper we analyse currently used predictive policing software packages with respect to common problems of data mining, and describe challenges that arise in the context of their socio-technical application.     

Understanding the spatial distribution of crime based on its related variables using geospatial discriminative patterns

Crime tends to cluster geographically. This has led to the wide usage of hotspot analysis to identify and visualize crime. Accurately identified crime hotspots can greatly benefit the public by creating accurate threat visualizations, more efficiently allocating police resources, and predicting crime. Yet existing mapping methods usually identify hotspots without considering the underlying correlates of crime. In this study, we introduce a spatial data mining framework to study crime hotspots through their related variables. We use Geospatial Discriminative Patterns (GDPatterns) to capture the significant difference between two classes (hotspots and normal areas) in a geo-spatial dataset. Utilizing GDPatterns, we develop a novel model—Hotspot Optimization Tool (HOT)—to improve the identification of crime hotspots. Finally, based on a similarity measure, we group GDPattern clusters and visualize the distribution and characteristics of crime related variables. We evaluate our approach using a real world dataset collected from a northeast city in the United States.     

Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach

Regional electricity demand in Japan and spatial interaction among the regions using a Bayesian approach were examined. A spatial autoregressive (SAR) ARMA model was proposed to consider the features of electricity demand in Japan and a strategy of Markov chain Monte Carlo (MCMC) methods was constructed to estimate the parameters of the model. From empirical results, the spatial autoregressive ARMA (1, 1) model was selected, and it was found that spatial interaction plays an important role in electricity demand in Japan. Moreover, log predictive density showed that this SAR-ARMA model performs better than a univariate ARMA model. It was confirmed that the space-time model improves the performance of forecasting future electricity demand in Japan.     

Incorporating space and time into random forest models for analyzing geospatial patterns of drug-related crime incidents in a major U.S. metropolitan area

The opioid crisis has hit American cities hard, and research on spatial and temporal patterns of drug-related activities including detecting and predicting clusters of crime incidents involving particular types of drugs is useful for distinguishing hot zones where drugs are present that in turn can further provide a basis for assessing and providing related treatment services. In this study, we investigated spatiotemporal patterns of more than 52,000 reported incidents of drug-related crime at block group granularity in Chicago, IL between 2016 and 2019. We applied a space-time analysis framework and machine learning approaches to build a model using training data that identified whether certain locations and built environment and sociodemographic factors were correlated with drug-related crime incident patterns, and establish the top contributing factors that underlaid the trends. Space and time, together with multiple driving factors, were incorporated into a random forest model to analyze these changing patterns. We accommodated both spatial and temporal autocorrelation in the model learning process to assist with capturing the changes over time and tested the capabilities of the space-time random forest model by predicting drug-related activity hot zones. We focused particularly on crime incidents that involved heroin and synthetic drugs as these have been key drug types that have highly impacted cities during the opioid crisis in the U.S.     

A data-driven agent-based simulation to predict crime patterns in an urban environment

Spatial crime simulations contribute to our understanding of the mechanisms that drive crime and can support decision-makers in developing effective crime reduction strategies. Agent-based models that integrate geographical environments to generate crime patterns have emerged in recent years, although data-driven crime simulations are scarce. This article (1) identifies numerous important drivers of crime patterns, (2) collects relevant, openly available data sources to build a GIS-layer with static and dynamic geographical, as well as temporal features relevant to crime, (3) builds a virtual urban environment with these layers, in which individual offender agents navigate, (4) proposes a data-driven decision-making process using machine-learning for the agents to decide whether to engage in criminal activity based on their perception of the environment and, finally, (5) generates fine-grained crime patterns in a simulated urban environment. The novelty of this work lies in the various large-scale data layers, the integration of machine learning at individual agent level to process the data layers, and the high resolution of the resulting predictions. The results show that the spatial, temporal, and interaction layers are all required to predict the top street segments with the highest number of crimes. In addition, the spatial layer is the most informative, which means that spatial data contributes most to predictive performance. Thus, these findings highlight the importance of the inclusion of various open data sources and the potential of theory-informed, data-driven simulations for the purpose of crime prediction. The resulting model is applicable as a predictive tool and as a test platform to support crime reduction.     

Technology trends analysis and forecasting application based on decision tree and statistical feature analysis

Analyzing mass information and supporting foresight are very important task but they are extremely time-consuming work. In addition, information analysis and forecasting about the science and technology are also very critical tasks for researchers, government officers, businessman, etc. Some related studies recently have been executed and semi-automatic tools have been developed actively. Many researchers, annalists, and businessmen also generally use those tools for strategic decision making. However, existing projects and tools are based on subjective opinions from several experts and most of tools simply explain current situations, not forecasting near future trends. Therefore, in this paper, we propose a technology trends analysis and forecasting model based on quantitative analysis and several text mining technologies for effective, systematic, and objective information analysis and forecasting technology trends. Additionally, we execute a comparative evaluation between the suggested model and Gartner’s forecasting model for validating the suggested model because the Gartner’s model is widely and generally used for information analysis and forecasting.     

An indicator set for capturing long-term open space fragmentation and urban development dynamics

Open space preservation is an important spatial policy issue in developed, densely populated countries. Understanding open space dynamics in relation to urban development is critical to the development and evaluation of such policies. Starting from a conceptual model that hypotheses how open space will have developed over the past century we use a set of landscape ecology based indicators to capture these changes from a societal (perceptional) perspective. The indicators are applied to a newly compiled geodatabase of urban development in the Netherlands to show their performance in detecting and understanding past and future trends in open space provision.Our combined indicator set consists of land-use-based metrics that capture the area ratio of open space in relation to the total available space and total unit density of open and built-up patches. The methodology is designed to fit the low spatial and thematic resolution of land-use models as is exemplified by the inclusion of a future land-use scenario in the evaluation of open space development. The indicators confirm the hypothesised intrusion, intermediate and fill-up phases in open space fragmentation, and indicate a strong correlation between fragmentation and loss of open area. The results facilitate the distinguishing of compact urban regions from more fragmented counterparts while taking their relative state of urban development into account.The combined indicator set is useful to summarise and compare spatial development status between regions, while in combination with the advent of more detailed historic land-use data, our approach can be used to analyse open-space dynamics in different socioeconomic contexts.     

基于夜间灯光的2000—2018年成渝地区城市化过程研究

成渝城市群正逐步成为我国西部经济发展的增长极,探索其城市化时空格局对区域协调发展具有指引作用。基于2000—2018年整合夜光遥感数据提取城市群多期建成区空间范围,运用夜光规模统计、标准椭圆、位序—规模法则以及空间自相关等指标、模型定量分析了成渝区域城市化时空过程。结果表明：(1)灯光与统计数据配合下的建成区提取多年平均误差为1.27%,重庆、成都和绵阳市提取验证效果好;(2)19年间成渝各城市夜光规模显著增长,整体累计增长5.6倍,2010年后成渝城市群灯光规模扩张速度显著;(3)区域内各城市的位序—规模（rank-size）由高位序城市集中发展转向区域协调均衡发展,中小城市均有不同程度的扩张;(4)城市群规模重心位于四川资阳市安岳县,重心移动整体上以东南方向为主,空间格局整体呈现以“成都—重庆”为轴线沿西北向东南演变,空间范围逐渐扩张,说明以重庆为主的东南都市圈的社会经济形势更显著,对城市群发展更具影响力;(5)成渝城市群扩展的空间集聚程度逐渐加强,冷热点格局整体呈现冷点区占比大,热点区占比低的特征,热点区主要出现在位于成都与重庆主城区及其周边城镇。研究揭示了成渝城市群均衡发展的特...     

Estimating quality of life dimensions from urban spatial pattern metrics

The spatial structure of urban areas plays a major role in the daily life of dwellers. The current policy framework to ensure the quality of life of inhabitants leaving no one behind, leads decision-makers to seek better-informed choices for the sustainable planning of urban areas. Thus, a better understanding between the spatial structure of cities and their socio-economic level is of crucial relevance. Accordingly, the purpose of this paper is to quantify this two-way relationship. Therefore, we measured spatial patterns of 31 cities in North Rhine-Westphalia, Germany. We rely on spatial pattern metrics derived from a Local Climate Zone classification obtained by fusing remote sensing and open GIS data with a machine learning approach. Based upon the data, we quantified the relationship between spatial pattern metrics and socio-economic variables related to ‘education’, ‘health’, ‘living conditions’, ‘labor’, and ‘transport’ by means of multiple linear regression models, explaining the variability of the socio-economic variables from 43% up to 82%. Additionally, we grouped cities according to their level of ‘quality of life’ using the socio-economic variables, and found that the spatial pattern of low-dense built-up types was different among socio-economic groups. The proposed methodology described in this paper is transferable to other datasets, levels, and regions. This is of great potential, due to the growing availability of open statistical and satellite data and derived products. Moreover, we discuss the limitations and needed considerations when conducting such studies.     

Urban expansion using remote-sensing data and a monocentric urban model

The expansion of urban land use is an indispensable tool of urban policy, particularly in developing countries which are facing rapid urbanization. However, most sophisticated spatial-analysis methods used in developed countries are not applicable to developing countries due to the limited availability of spatially-resolved statistical data. This study examines the applicability of a simple, classic monocentric urban model for capturing urban activities in three Chinese cities in 2003 and 2013. The model estimates traffic cost, goods consumption, area of available residential floor space, and rents for residential floor space by aggregating urban statistics and estimating urban areas using remote sensing data. We find that some predictions of our model are inaccurate due to its simple, static nature; however, the model predicts meaningful temporal trends in key variables and meaningful differences among the population and economic growth trajectories of the three cities. Our results imply that the classic monocentric model is useful for approximating urban activities and for strategic planning of urban traffic policies, as it allows tracing of the causal mechanisms of urban activity and furnishes estimates for several critical statistical measures needed for policy evaluation. Although a monocentric model may not allow detailed representation of urban activity, we show that our simple model nonetheless offers advantages for studying urban planning practices in developing nations in which the availability of statistical data is limited.     

A global analysis of urban reflectance

A comparative Spectral Mixture Analysis (SMA) of Landsat 7 Enhanced Thematic Mapper (ETM+) imagery for a collection of 28 urban areas worldwide provides a physical basis for a spectral characterization of urban reflectance properties. These urban areas have similar mixing space topologies and can be represented by three‐component linear mixture models in both scene‐specific and global composite mixing spaces. The results of the analysis indicate that the reflectance of these cities can be accurately described as linear combinations of High Albedo, Dark and Vegetation spectral endmembers within a two‐dimensional mixing space containing over 90% of the variance in the observed reflectance. Only two of the 28 cities had greater than 10% median RMS misfit to the three‐endmember linear model. The relative proportions of these endmembers vary considerably among different cities and within individual cities but in all cases the reflectance of the urban core lies near the dark end of a mixing line between the High Albedo and Dark endmembers. The most consistent spectral characteristic of the urban mosaic is spectral heterogeneity at scales of 10–20?m. In spite of their heterogeneity, built‐up areas do occupy distinct regions of the spectral mixing space. This localization in mixing space allows spectrally mixed pixels in built‐up areas to be discriminated from undeveloped land cover types. This provides a basis for mapping the spatial extent of human settlements using broadband optical satellite imagery collected over the past 30 years.     

Urban growth modeling of Kathmandu metropolitan region, Nepal

The complexity of urban system requires integrated tools and techniques to understand the spatial process of urban development and project the future scenarios. This research aims to simulate urban growth patterns in Kathmandu metropolitan region in Nepal. The region, surrounded by complex mountainous terrain, has very limited land resources for new developments. As similar to many cities of the developing world, it has been facing rapid population growth and daunting environmental problems. Three time series land use maps in a fine-scale (30 m resolution), derived from satellite remote sensing, for the last three decades of the 20th century were used to clarify the spatial process of urbanization. Based on the historical experiences of the land use transitions, we adopted weight of evidence method integrated in cellular automata framework for predicting the future spatial patterns of urban growth. We extrapolated urban development patterns to 2010 and 2020 under the current scenario across the metropolitan region. Depending on local characteristics and land cover transition rates, this model produced noticeable spatial pattern of changes in the region. Based on the extrapolated spatial patterns, the urban development in the Kathmandu valley will continue through both in-filling in existing urban areas and outward rapid expansion toward the east and south directions. Overall development will be greatly affected by the existing urban space, transportation network, and topographic complexity.     

Urbanization pressure and modeling of urban growth: example of the Tunis Metropolitan Area

Urbanization process is a major factor of change in the Mediterranean region where pre-urban cities and new urban settlements have raised over the past decades. Several cities rapidly became regional centres or international nodes according to economic and political pressures. Urbanization (and informal settlement) causes land cover changes which can lead to deeper social, economic and environmental changes. The main objective of this paper is to provide time-series information to define and locate the evolution trends of the Tunis Metropolitan Area. In a first step, satellite imagery has been used (1986-1996 SPOT XS) to extract the land cover, identify the urbanization processes and estimate the changes. One of the main aspects is to locate informal settlement areas that grow significantly along the roadway networks in the Tunisian capital. Results show a global progression of the built-up areas of 13% in 10 years. In a second step, the urban growth evolution has been approached by using a potential model that provides general trends of feasible urban expansion, taking into account protection laws of natural and agricultural land. This type of model has not been tested on developing cities and as such it corresponds to a new planning contribution for planners who have no concept of spatially how their urban areas have changed over time and where the growth is occurring. In this case, it has been calibrated over the period of 1986-1996, and then applied to predict the location of the built-up growth over the next 10 years (1996-2006). These results can provide local authorities and other stakeholders with information towards decision support documents for future planning and monitoring plans. Moreover, they can be updated systematically through the integration of remote sensing data.