Dense Urban Regions Research Articles

Impact of 100% vehicle electrification on the distribution grid in dense urban regions

Vehicle electrification is a requisite for cities to combat global warming. A key challenge associated with the wide-scale adoption of electric vehicles (EVs) is the impact of charging on distribution networks. The problem is further exacerbated in dense urban regions, where space is intrinsically limited. If the grid infrastructure is too constrained, it will dictate locations where EV chargers can be deployed without the need for costly infrastructure upgrades. In dense urban regions, this may hinder or slow down adoption. This paper investigates the impact of EV charging on the distribution grid in dense urban regions, with a focus on a case study based in Harlem, a neighborhood in Manhattan, New York City (NYC). The impact of EV charging on the distribution grid depends on several factors, including the number of charging stations, their locations, and the charging demand they result in, which also relate to consumers’ charging behavior. To evaluate this impact, load flow analysis was carried out for a futuristic scenario assuming all personal cars become EVs, with a focus on voltage drop and transformer/feeder overloading. It was found that with proper siting of chargers and scheduling for charging the distribution grid can handle a 100% EV scenario without infrastructure upgrade.

Community-centric distributed energy resources for energy justice and decarbonization in dense urban regions

Low-to-moderate income (LMI) households suffer from higher energy cost burden as they spend a larger portion of their income on electricity bills than higher-income households due to factors including limited access to renewable energy. Although distributed energy resources (DERs) can be owned by individuals, supposedly enabling energy justice, reality shows otherwise since it requires capital investment and property ownership. To mitigate these barriers, the paper proposes “Community Energy Cells” (CECs) which consists of a group of DERs and controllable loads in proximity, represented by a Cell Aggregator (CA) as a single controllable entity. CECs can operate independently during disruptions, deploy renewable energy to LMI households, and provide multiple value streams for their DERs, including the option to aggregate and sell in the wholesale energy market. A financial case study using National Renewable Energy Laboratory’s (NREL) System Advisory Model (SAM) was performed focusing on select buildings in West Harlem, New York City, with approximate 2.5 MW solar and 15 MW storage potentials. Considering that their CA benefits from investment tax credits, and the costs including installation and electricity purchase, it has a net present value of over $2 M, an internal rate of return of 35 % after 25 years, and sufficient cash flow to cover debt service. These returns are sensitive to key values: battery price and committed capacity for incentive programs for DERs and loads. The realization of CEC has interdisciplinary challenges that the paper elaborates on. Nevertheless, CEC is indispensable for tackling climate change and resiliency while attaining energy justice.

Train to Vehicle: Toward Sustainable Transportation in Dense Urban Regions

This article investigates the feasibility of using regenerative energy from braking trains to charge electric buses in the context of New York City’s (NYC) subway and electric bus networks. A case study centered around NYC’s system has been performed to evaluate the benefits and challenges pertaining to the use of the preexisting subway network as a power supply for its new all-electric buses. The analysis shows that charging electric buses via the subway system during subway off-peak periods does not hinder regular train operation. In addition, having the charging electric buses connected to the third rail allows for more regenerative braking energy (RBE) to be recuperated, decreasing the energy wasted throughout the system. It was also found that including a wayside energy storage system (WESS) reduces the overall substation peak power consumption.

Energy storage systems for commercial buildings in dense urban regions: NYC case study

In response to the mounting interest in Battery Energy Storage Systems (BESS) from a wide range of entities—commercial, private, and governmental—this paper analyzes the decision-making criteria for BESS implementation. The structure of electricity bills are analyzed to understand the current cost structure of building utility tariffs. In addition, the potential revenue streams provided by various incentive programs, including demand response contracts, when implementing BESS were investigated. Highlighting the need for a pragmatic and efficient ranking system, we propose a model that leverages load profile data to determine the suitability of BESS implementation within a portfolio of buildings. This approach offers property owners a streamlined method to prioritize which buildings to equip with BESS. To validate the efficacy of our load profile-based model, we compare it against established financial models, specifically employing the System Advisory Model (SAM) from the National Renewable Energy Lab (NREL). Our findings indicate that a load profile-centric ranking system can effectively determine the most appropriate buildings for ESS implementation. This paper provides a substantial contribution to the field of BESS sizing, offering a simplified yet robust methodology for ranking that aligns well with more complex financial models. By focusing on the practical utility of load profile data, we enable a simplified and informed process for property managers to make sound decisions in the deployment of energy storage systems.

Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework

The transformation to net zero-energy (nZE) building stocks involves harvesting of renewable energy, enhancement of building energy efficiency, incorporation of various supply-side options, and advanced energy management. Although several studies have evaluated the feasibility of nZE for residential areas in cold regions, only a few studies have been conducted on dense urban regions in noncold climate. Additionally, the accommodation of such building stock transformation by the current power distribution network has not been clarified to date. Thus, this study proposes a novel framework integrating synthetic population, activity, building stock, and power distribution network to explore transformation pathways. This was further demonstrated by a case study on a densely populated region covering four cities in Tokyo, Japan. The results signified that complete dissemination of popular energy efficiency measures can reduce the energy demand by 40%. With the reduction in energy demand, nearly nZE condition can be realized only if the building roof area is completely covered by photovoltaics. In particular, one half of the generation would be utilized locally, whereas the remaining would be exported. Moreover, the power distribution network can accommodate the transformation with a slight increase in power distribution loss up to 1% and a modest reinforcement requirement owing to line overloading. Conclusively, this study revealed that the transformation and reinforcement requirement are significantly distinct from those required in cold regions.

Spectral mixture analysis of AVIRIS-NG hyperspectral data for material identification and classification for the part of Kolkata city

Identification and extraction of material in the urban scenario are always challenging due to the presence of different heterogeneous materials. Airborne hyperspectral sensors with the advantage of both high spectral and spatial resolution can be used in such areas for the identification and mapping of urban materials. Manual extraction of the endmembers in highly dense heterogeneous urban regions is a very difficult task to achieve, but with automated extraction techniques, it yields promising results. To counter the mixed pixels problem in heterogeneous urban areas automated endmember extraction techniques like Endmember Average RMS (EAR), Minimum Average Spectral Angle (MASA), and Count Based Endmember Selection (CoB) was used for pure endmember identification in the densely populated urban area of Kolkata region using AVIRIS NG imagery. Spectral Unmixing Multiple Endmember Spectral Mixture Analysis (MESMA), Linear Spectral Unmixing (LSU), and Mixture Tuned Matched Filtering (MTMF) was performed on AVIRIS NG data using the extracted endmembers of different urban materials. The rule-based classification was performed on abundance images obtained from MESMA, MTMF, and LSU techniques for comparative analysis. Classification using MESMA abundance images outperformed in mapping mixed urban environments with an overall accuracy (OA) of 88.6 % followed by the rule-based classified output of MTMF and LSU with OA of 75.27% and 72.45% respectively.

ENRICHMENT OF UAV PHOTOGRAMMETRIC POINT CLOUD TO ENHANCE DSM IN A DENSE URBAN REGION

Abstract. Unmanned Arial Vehicle (UAV) have become very cost effective and time efficient technology for surveying, mapping and various other geospatial applications. UAV serves as a platform but the main dependency also lies on which type of the sensor is integrated on it. Optical photogrammetric surveys are more often conducted for generalized purposes and for its advantages. UAV photogrammetry have become very useful tool in creation of 3D city model or Digital twin model. But creating a Digital Surface Model (DSM) through photogrammetry is not that efficient when it comes to very dense urban regions. In this research, the quality of DSM, Noise in the Photogrammetric point cloud are studied, then an experimental approach is tried to enhance the DSM than that of DSM obtained from photogrammetric processing software. From the results, it is observed that the quality of DSM is enhanced in narrow streets/roads, near trees, regions between trees and buildings, regions near building and road, etc. This approach can be utilized for low-cost UAV photogrammetry for accurate 3D modelling of the narrow streets or roads are of critical importance.

A comparison study on the role of urbanization in altering the short-duration and long-duration intense rainfall

Urbanization has significantly changed the regional hydrological cycle and energy balance. However, the different roles of urbanization on intense rainfall with short and long persistence need a better understanding, particularly in coastal mega cities with complex terrains. In this study, we compared the spatial and diurnal characteristics of intense rainfall across two coastal cities with different degrees of urbanization with 70 meteorological stations. The effects of anthropogenic and geographical factors on short-duration intense rainfall (SDIR) and long-duration intense rainfall (LDIR) were investigated using the statistical method. SDIR and LDIR events show different spatial patterns that SDIR events center in the highly urbanized regions while LDIR events center in the coastal mountainous regions. In terms of diurnal features, it is found that the higher occurrence frequency of SDIR in the dense urban region than that in suburbs occurs over the period with strong urban heat island intensity (UHII). It indicated that thermal contrast between urban and suburbs breeds an atmospheric environment for inspiriting the convection and SDIR events. The urban-induced increase in SDIR events depended on urbanization stages, with only dense urban regions showing a significant influence. The results of geographical detector model (GDM) also demonstrated that the synergy of build-up area and population explained 48 %–51 % of spatial heterogeneity of SDIR. Nevertheless, urbanization has little effect in modifying the diurnal features of LDIR events, while it might influence spatial rainfall patterns by enhancing rainfall peaks. The GDM results indicated that terrain positively dominates the spatial distribution for LDIR events, and the interactions of multi-factors (terrain, urbanization, or distance to the coast et al.) have enhanced explanatory power (q values up to 0.70). The results provide a fundamental understanding for the effects of anthropogenic and geographical factors on different types of rainfall events in coastal mega cities.

It's worth a shot: urban density, endogenous vaccination decisions, and dynamics of infectious disease.

We develop an agent-based model of vaccine decisions across a heterogeneous network model with urban and rural regions. In the model, agents make rational decisions to vaccinate or not, based on the relative private costs of vaccinations and infections as well as an estimated probability of infection if not vaccinated. The model is a methodological advance in that it provides an economic rationale for traditional threshold models of vaccine decision-making that are commonly used in agent-based network models of vaccine choice. In the model, more dense urban regions have more connections between agents than less dense rural regions. Higher density leads to higher levels of vaccine usage and lower rates of infection in urban regions within the model. This finding adds to the more commonly discussed socio-economic reasons for higher levels of vaccination usage in urban areas compared to rural areas. In addition to this direct contribution, the paper emphasizes the importance of endogenous decision-making in models of epidemiology. For instance, we find that networks that lead to larger epidemics in exogenous vaccination models lead to smaller epidemics in our model because agents use vaccinations to offset the additional risk introduced by these network structures. Endogenous agent responses to risk need to be incorporated into theoretical and empirical models of economic epidemiology.

Green Densities: Accessible Green Spaces in Highly Dense Urban Regions—A Comparison of Berlin and Qingdao

Recreational green spaces are associated with human thriving and well-being. During the ongoing COVID-19 pandemic a spotlight has been shed on the interconnection between access to these spaces, human well-being and social equity. Containment measures enacted in many cities effectively precluded people from reaching distant recreational areas during the pandemic and consequently, recreational areas close to home became particularly important. Urban density is often associated with building or population density with the assumption that if either parameter has a high value, the availability of open (green) space is low. Certain densities are associated with specific spatial qualities. Addressing challenges of sustainable development, a detailed evaluation of density is necessary to allow evidence-based arguments, planning and decision-making. In this study we develop a multi-scale analysis method for quantifying and assessing green infrastructures from settlement unit to building level to reach a differentiated view on density, arguing that density can be organized in different ways achieving very different qualities. For this purpose, we use geospatial-data analysis and in-depth neighborhood studies to compare two cities in Asia and Europe, revealing different ways of organizing density in the built environment and identifying a derivation of approaches for sustainable development in dense urban regions.

ANALYZING THE IMPACT OF BUILT UP AND GREEN SPACES ON LANDSURFACE TEMPERATURE WITH SATELLITE IMAGES IN JALANDHAR SMART CITY

Land use in developing nations is shifting as a result of rapid urbanisation. The ecosystem is being harmed by the unplanned, urban development of cities. Utilizing data from Landsat, the present research examined the spatiotemporal urban growth in Jalandhar City and its impact on variations in land surface temperature (LST). The results indicate that urban regions have increased while urban green spaces have shrunk (UGS). UGS and LST have been found to be inversely correlated. Where there was a low percentage of urban areas and a high percentage of accessible green spaces, it was observed that the LST decreased. The link between spectral variability and changes in vegetation growth rate was studied using the NDVI (Normalized Difference Vegetation Index). It works on tracking the growth of green vegetation and spotting variations in the amount of greenery. Where NDVI value was low, the scatter plots indicate higher surface temperatures. The dense urban regions with little available vegetation cover were linked to the low NDVI number and the NDVI is negative in the urban area of Jalandhar. High NDVI values indicate regions with dense vegetation and low surface temperatures. The research findings of present study could be applied to urban administration, planning, management and research projects.

Automatic Concrete Damage Recognition Using Multi-Level Attention Convolutional Neural Network.

There has been an increase in the deterioration of buildings and infrastructure in dense urban regions, and several defects in the structures are being exposed. To ensure the effective diagnosis of building conditions, vision-based automatic damage recognition techniques have been developed. However, conventional image processing techniques have some limitations in real-world situations owing to their manual feature extraction approach. To overcome these limitations, a convolutional neural network-based image recognition technique was adopted in this study, and a convolution-based concrete multi-damage recognition neural network (CMDnet) was developed. The image datasets consisted of 1981 types of concrete surface damages, including surface cracks, rebar exposure and delamination, as well as intact. Furthermore, it was experimentally demonstrated that the proposed model could accurately classify the damage types. The results obtained in this study reveal that the proposed model can recognize the different damage types from digital images of the surfaces of concrete structures. The trained CMDnet demonstrated a damage-detection accuracy of 98.9%. Moreover, the proposed model could be applied in automatic damage detection networks to achieve superior performance with regard to concrete surface damage detection and recognition, as well as accelerating efficient damage identification during the diagnosis of deteriorating structures used in civil engineering applications.

Spatial sorting of innovative firms and heterogeneous effects of agglomeration on innovation in Germany

We examine the effects of agglomeration, differentiating between urbanization and localization economies, on four distinct types of innovation in manufacturing and services. Furthermore, estimating multilevel panel regression models, we investigate the sorting of highly innovative firms into dense urban and/or specialized regions by considering both observable and unobservable firm characteristics. The results indicate that spatial sorting is important. A large portion of the regional differences in firm innovation rates is due to firm characteristics. Estimates that ignore unobserved heterogeneity at the firm level still point to a positive and significant impact of localization economies on different types of innovation. However, once we include firm fixed effects and distinguish between manufacturing and services, only some weak indication for positive effects of localization on radical innovations of manufacturing firms remains. In addition, the probability to adopt an existing product by an manufacturing firm seems to be positively influenced by urbanization economies. For the service sector, in contrast, we find adverse effects of localization on different kinds of innovation and no important effect of urbanization.

Road Network Extraction from Low-Frequency Trajectories Based on a Road Structure-Aware Filter

Many studies have utilized global navigation satellite system (such as global positioning system (GPS)) trajectories in order to successfully infer road networks because such data can reveal the geometry and development of a road network, can be obtained in a timely manner, and updated on a low budget. Unfortunately, existing studies for inferring road networks from vehicle traces suffer from low accuracy, especially in dense urban regions and locations with complex structures, such as roundabouts, overpasses, and complex intersections. This study presents a novel two-stage approach for inferring road networks from trajectory points and capturing road geometry with better accuracy. First, a lane structure-aware filter is proposed to cluster vehicle trajectories influenced by high noise and outliers in order to reveal the continuous structure points of lane curves from massive trajectory points. Second, a road tracing operator is utilized to segment the road network geometry by inserting new vertices and segments to a vigorous vertex in the heading of the structure points that are extracted in the first step. Experimental results demonstrate the increased accuracy of the extracted roads and show that the proposed method exhibits strong robustness to noise and various sampling rates.

Techno-economic BIPV evaluation method in urban areas

Photovoltaics have been proven to be one of the key technologies of electricity generation at building, district, and city level. Numerous strategies have been applied to maximize the performance of roof mounted PV panels, while the potential of building façades for PV installation is often disregarded, especially in urban areas. However, façade mounted PVs could provide high energy generation and be economically feasible especially in dense urban regions with low rooftop areas. This study presents an integrated techno-economic evaluation tool, to help policy makers and investors identify the most appropriate PV installation surfaces in urban areas. Moreover, the installation surfaces could be prioritized based on energy generation, economic profitability, and carbon footprint reduction. Results show that installation of façade mounted PV panels with optimum angles increases the energy generation and IRR up to 19% and 6% in south facades. The presented tool provides architectural designers, as well as planning authorities, a method to quantify solar planning decisions at the building and urban scale.

Technology Leveraging for Infrastructure Asset Management: Challenges and Opportunities

Transportation and other infrastructure systems, particularly in dense urban regions, are intertwined, interdependent, multi-scale, multi-domain and complex, and their behavior cannot be predicted even when element behaviors are known. Such systems should be managed just like financial assets, leveraging measurement-based, objective and reliable metrics for documenting their value, performance and condition, and based on their lifecycle and disutility risk for each distinct limit-states of performance as discussed in the following. In this paper writers attempt to offer a perspective for asset management of civil infrastructures with a focus on highway bridges and describe the tools that are considered necessary for rectifying the current shortcomings mainly arising from subjective and incomplete performance and condition evaluation practice. The adoption of sensing systems, which allows measurements of displacement, acceleration, strain, tilt and that can be collected wirelessly, has the potential of providing objective metrics needed for optimal asset management. The authors however caution that such a transition (from asset management based on visual inspection to data-driven asset management based on objective metrics) could be truly achieved only if combined with the proper training of a new generation of infrastructure inspectors and stakeholders. The paper attempts to provide a roadmap to achieve such a transition in asset management and describes the critical concepts that should be incorporated in training a new generation of civil engineers in charge of maintaining our transportation assets.

Seeing the Forest through the Trees: Sociocultural Factors of Dense Urban Spaces

Coming to terms with the complexity of dense urban areas represents one of the major challenges people, organizations and governments will face in the next few decades. Defining, explaining and modeling socio-cultural factors associated with the development of dense urban regions will be among the most complex problems researchers will face when studying dense urban areas. In this paper, we seek to open the discussion and begin to define the modeling process by conducting a literature review and creating a conceptual framework based on Verba, Binder, Coleman, La Palombara, Pye, & Weiner’s (1971) model of political development. The model emphasizes six key elements of political development, which we use as a point of departure to begin to identify key socio-cultural factors of dense urban areas. Our framework also embarks on identifying a difference between factor relationships in loosely and tightly integrated cities. The interrelationship between variables and the recursive nature of variables are some of the major difficulties we identify when it comes to modeling sociocultural dynamics in dense urban areas.

Constraining the uncertainty in emissions over India with a regional air quality model evaluation

To evaluate uncertainty in the spatial distribution of air emissions over India, we compare satellite and surface observations with simulations from the U.S. Environmental Protection Agency (EPA) Community Multi-Scale Air Quality (CMAQ) model. Seasonally representative simulations were completed for January, April, July, and October 2010 at 36 km × 36 km using anthropogenic emissions from the Greenhouse Gas-Air Pollution Interaction and Synergies (GAINS) model following version 5a of the Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants project (ECLIPSE v5a). We use both tropospheric columns from the Ozone Monitoring Instrument (OMI) and surface observations from the Central Pollution Control Board (CPCB) to closely examine modeled nitrogen dioxide (NO2) biases in urban and rural regions across India. Spatial average evaluation with satellite retrievals indicate a low bias in the modeled tropospheric column (−63.3%), which reflects broad low-biases in majority non-urban regions (−70.1% in rural areas) across the sub-continent to slightly lesser low biases reflected in semi-urban areas (−44.7%), with the threshold between semi-urban and rural defined as 400 people per km2. In contrast, modeled surface NO2 concentrations exhibit a slight high bias of +15.6% when compared to surface CPCB observations predominantly located in urban areas. Conversely, in examining extremely population dense urban regions with more than 5000 people per km2 (dense-urban), we find model overestimates in both the column (+57.8) and at the surface (+131.2%) compared to observations. Based on these results, we find that existing emission fields for India may overestimate urban emissions in densely populated regions and underestimate rural emissions. However, if we rely on model evaluation with predominantly urban surface observations from the CPCB, comparisons reflect model high biases, contradictory to the knowledge gained using satellite observations. Satellites thus serve as an important emissions and model evaluation metric where surface observations are lacking, such as rural India, and support improved emissions inventory development.

Estimating disease burden of a potential A(H7N9) pandemic influenza outbreak in the United States

BackgroundSince spring 2013, periodic emergence of avian influenza A(H7N9) virus in China has heightened the concern for a possible pandemic outbreak among humans, though it is believed that the virus is not yet human-to-human transmittable. Till June 2017, A(H7N9) has resulted in 1533 laboratory-confirmed cases of human infections causing 592 deaths. The aim of this paper is to present disease burden estimates (measured by infection attack rates (IAR) and number of deaths) in the event of a possible pandemic outbreak caused by human-to-human transmission capability acquired by A(H7N9) virus. Even though such a pandemic will likely spread worldwide, our focus in this paper is to estimate the impact on the United States alone.MethodThe method first uses a data clustering technique to divide 50 states in the U.S. into a small number of clusters. Thereafter, for a few selected states in each cluster, the method employs an agent-based (AB) model to simulate human A(H7N9) influenza pandemic outbreaks. The model uses demographic and epidemiological data. A few selected non-pharmaceutical intervention (NPI) measures are applied to mitigate the outbreaks. Disease burden for the U.S. is estimated by combining results from the clusters applying a method used in stratified sampling.ResultsTwo possible pandemic scenarios with R0 = 1.5 and 1.8 are examined. Infection attack rates with 95% C.I. (Confidence Interval) for R0 = 1.5 and 1.8 are estimated to be 18.78% (17.3–20.27) and 25.05% (23.11–26.99), respectively. The corresponding number of deaths (95% C.I.), per 100,000, are 7252.3 (6598.45–7907.33) and 9670.99 (8953.66–10,389.95).ConclusionsThe results reflect a possible worst-case scenario where the outbreak extends over all states of the U.S. and antivirals and vaccines are not administered. Our disease burden estimations are also likely to be somewhat high due to the fact that only dense urban regions covering approximately 3% of the geographic area and 81% of the population are used for simulating sample outbreaks. Outcomes from these simulations are extrapolated over the remaining 19% of the population spread sparsely over 97% of the area. Furthermore, the full extent of possible NPIs, if deployed, could also have lowered the disease burden estimates.

Quantifying the Overlap between Cadastral and Visual Boundaries: A Case Study from Vanuatu

Cadastres are argued as an essential tool to support land tenure security. Low cadastral coverage in developing countries creates a driver for innovative methods to expedite the mapping processes. As a human construct, the morphology of parcel boundaries is a diverse and complex topic: there are limited generalized rules for identifying, describing, and classifying them. This paper studies both the institutional and spatial aspects of cadastral boundaries, in order to provide more contemporary knowledge about the morphology of cadastral boundaries. This study inspects the relationship between topographic objects and general boundaries in the case context of Port Vila, Vanuatu. Statistical analysis reveals that under a dialectical error tolerance, large percentages of cadastral boundaries coincide with topographic objects. Specifically, in dense urban regions, road edges and building walls coincide with the majority of cadastral boundaries, with proportions of 49% and 35%, respectively. In suburban regions, the fence (25%), instead of buildings, plays an important role in marking a parcel border. The landscape is observed to have significant impact on parcel morphology. Therefore, constructing a map based on automatic or semi-automatic identification and classification of these features could significantly contribute to cadastral mapping in developing countries.