A comparative assessment of local municipal food policy integration in the Netherlands

ABSTRACT

Local governments around the world increasingly engage in food governance, aiming to address food system challenges such as obesity, food waste, or food insecurity. However, the extent to which municipalities have actually integrated food across their policies remains unknown. This study addresses this question by conducting a medium-n systematic content analysis of local food policy outputs of 31 Dutch municipalities. Policy outputs were coded for the food goals and instruments adopted by local governments. Our analysis shows that most municipalities integrate food to a limited extent only, predominantly addressing health and local food production or consumption. Furthermore, municipalities seem hesitant to use coercive instruments and predominantly employ informative and organizational instruments. Nonetheless, a small number of municipalities have developed more holistic approaches to address food challenges. These cities may prove to be a leading group in the development of system-based approaches in Dutch local food policy.     

Understanding the impact of High Speed Railway on urban innovation performance from the perspective of agglomeration externalities and network externalities

Transportation infrastructure and innovation play an important role in promoting regional economic growth. However, previous studies have mainly focused on the economic agglomeration by increased accessibility, but ignored the important on inter-city economic linkages and the network externalities it generated. Based on the externality perspective, this paper uses the propensity score matching model (PSM-DID) and spatial econometric model to analyze the heterogeneous impact of HSR opening on urban innovation performance and compare the differential effects of agglomeration externalities and network externalities on knowledge spillovers. The empirical results suggest the following: (1) The opening of HSR significantly improves urban innovation performance, but there is significant spatial heterogeneity. (2) The opening of HSR not only promotes the agglomeration of production factors to cities, but also strengthens inter-city economic connections, thus improving urban innovation performance through agglomeration externalities and network externalities. However, compared to agglomeration externalities, network externalities have a superior strength of impact on urban innovation performance. (3) From the dynamic effect perspective, the opening of HSR has a hysteresis effect on urban innovation of 2–3 years, and differences exist between cities.     

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a data-driven soft sensor, which can supplement the physical sensor. Artificial Neural Networks have long been used for time series forecasting given their ability to recognize patterns in the data. Long Short-Term Memory (LSTM) neural networks are equipped with memory gates to help them learn time dependencies in a data series and have been proven to outperform other type of networks in predicting water levels in urban drainage systems. When used for soft sensing, neural networks typically receive antecedent observations as input, as these are good predictors of the current value. However, the antecedent observations may be missing due to transmission errors or deemed anomalous due to errors that are not easily explained. This study quantifies and compares the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. We applied these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark. We observed that i) LSTM predictions generally displayed large variability across training runs, which may be reduced by improving the selection of hyperparameters (non-trainable parameters); ii) when the most recent observations were known, adding information on the past did not improve the prediction accuracy; iii) when gaps were introduced in the antecedent water depth observations, LSTM networks were capable of compensating for the missing information with the other available input features (time of the day and rainfall intensity); iv) LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors. Therefore, we concluded that LSTM neural network may be trained to act as soft sensors in urban drainage systems even when observations from the physical sensors are missing.     

Predicting pollutant removal in constructed wetlands using artificial neural networks(ANNs)

Growth in urban population,urbanisation,and economic development has increased the demand for water,especially in water-scarce regions.Therefore,sustainable approaches to water management are needed to cope with the effects of the urbanisation on the water environment.This study aimed to design novel configurations of tidal-flow vertical subsurface flow constructed wetlands(VFCWs) for treating urban stormwater.A series of laboratory experiments were conducted with semi-synthetic influent stormwater to examine the effects of the design and operation variables on the performance of the VFCWs and to identify optimal design and operational strategies,as well as maintenance requirements.The results show that the VFCWs can significantly reduce pollutants in urban stormwater,and that pollutant removal was related to specific VFCW designs.Models based on the artificial neural network(ANN) method were built using inputs derived from data exploratory techniques,such as analysis of variance(ANOVA) and principal component analysis(PCA).It was found that PCA reduced the dimensionality of input variables obtained from different experimental design conditions.The results show a satisfactory generalisation for predicting nitrogen and phosphorus removal with fewer variable inputs,indicating that monitoring costs and time can be reduced.     

Impact of Lake Michigan water level rise on complex bidirectional flow in the Chicago Area Waterway System (CAWS)

In the past decade, continuously rising water levels in Lake Michigan have been threatening lakefront areas, especially in metropolitan regions like the Greater Chicago area. This provides the motivation to analyze the impact that high lake levels have on the Chicago Area Waterway System (CAWS). As the only primary free connection between the CAWS and Lake Michigan, the Calumet Area waterway subsystem plays a key and unique role. In this work, a numerical model covering the Calumet subsystem and having Lake Michigan as a boundary condition, is set up, calibrated, and validated using limited field observations. It is found that the Calumet subsystem has become bidirectional, where both discharge and flow directions are controlled by lake levels. When lake levels are below −0.15 m (-0.5 ft, Chicago City Datum, CCD), the discharge in the Grand Calumet River is around zero, with water flowing along its east branch towards Indiana. When lake levels are above +0.46 m (+1.5 ft, CCD), the flow reverses direction and drains west into Illinois. In 2020, the mean lake-level was at +1.07 m (+3.5 ft, CCD), and the base discharge in the Grand Calumet River was approximately 8.5 m³/s (300 ft³/s). The higher Lake Michigan’s level is, the larger the discharge would be into Illinois. Potential impact of this extra discharge on Lake Michigan Diversion Accounting (LMDA) of the State of Illinois and flood management in the Chicago Sanitary and Ship Canal (CSSC), is analyzed; while the nature of the bidirectional flows is characterized with the intent of shedding light on this complex phenomenon.     

Study of traffic characteristics based on internet web real-time traffic conditions by image identification technology

With the rapid development of Internet, it is increasingly convenient to obtain real-time traffic condition information, which has greatly stimulated the improvement of urban traffic guidance. Traffic conditions are generally divided into four grades in the existing network platform, which are expressed in different colours. The understanding of traffic condition is still at the level of abstract senses. Therefore, it is difficult to grasp the characteristics of urban traffic. To this end, a new idea is proposed in this paper, and the new idea is to study the urban traffic characteristics based on real-time traffic condition information extraction with image identification technology. With this method, we can not only quantify the abstract traffic condition information, but also solve the loss of traffic condition information. In addition, an instance is analysed in this paper, it shows that it can provide references for urban traffic organization management very well.     

Modes of making smart cities: Or,practices of variegated smart urbanism

The ‘actually existing’ smart city is not a monolith. It is not directed by a universal logic, nor does it develop in a standardised way. As recent research has argued, the spatial, material, and political contexts of cities have major influence over what smart urbanism looks like in practice. This paper adds analytical depth to, and broadens the geographical scope of, research on the variegated modes of making smart cities. Based on empirical research in multiple Australian cities we use three case studies to explore three different modes of smart urbanism, each one centred on the interests of a different key actor: corporate-centric, citizen-centric, and planner-centric. These different modes can, and do, co-exist in the same city. At times, they are competing logics that fight to pull the city in different directions. Yet, they can also work together to shape smart city initiatives. In describing these different modes, we pay particular attention to the ways that these projects and strategies must contend with the already existing spatial, cultural, and political contexts of each place.     

Cloud asset for urban flood control

The performance of physical assets has become a major determinant success factor for urban flood control. However, managing these assets is always challenging as there are a huge number of diverse assets involved, which are distributed throughout the city, and owned by different agencies. Aiming at improving the management efficiency of these assets, and ensuring their performance, this paper proposes the concept of cloud asset based on cloud computing, mobile agent, and various smart devices. Through hardware integration and software encapsulation, cloud asset could sense its real-time status, adapt to varied working scenarios, be controlled remotely, and shared among agencies. It enables accurate real-time control of every asset, and thus improves the management efficiency and effectiveness. This paper first presents the concept of cloud asset with its technical architecture, and then analyses the software agent model for cloud asset, which is the key enabler to realize UPnP (Universal Plug and Play) management of assets, and provides mobility and intelligence for them. After that, the framework of cloud asset-enabled workflow management is built, in which cloud asset could be easily found and dynamically invoked by different workflows. Finally, a demonstrative case is provided to verify the effectiveness of cloud asset.     

Mining urban data (part A)

Modern cities are flooded with data. New information sources like public transport and wearable devices provide opportunities for novel applications that will improve citizens׳ quality of life. From a data science perspective, data emerging from smart cities give rise to a lot of challenges that constitute a new inter-disciplinary field of research. This article introduces the first part of a special issue on the topic ‘Mining Urban Data’ published in the journal Information Systems.     

The role of viscoelasticity on heterogeneous stress fields at frictional interfaces

We investigate the evolution of heterogeneous stress states along frictional interfaces. Using finite-element simulations, we model the occurrence of precursory slip sequences on a deformable-deformable as well as a deformable–rigid interface between two solids. Every interface rupture creates a stress concentration at its arrest position and erases the stress concentrations produced by previous slip events. While the interface is sticking perfectly between two slip events, erased stress concentrations reappear and survive several cycles of ruptures. Such reestablished stress concentrations are smaller than they were before the rupture. We show that the decrease rate of these stress concentrations is exponential with respect to the number of subsequent events and that the bulk viscoelasticity is at the origin of this history effect. During a slip event, the friction tractions at the interface change almost instantaneously, which leads, between two ruptures, to a relaxation-resembling viscous effect that restores the stress concentration. We describe the decrease rate analytically and highlight that it is proportional to the ratio of the viscous over the instantaneous Young’s moduli, and illustrate it by varying the material properties in our simulations.