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The practice of selling homes on contract—typically on onerous terms and comparatively riskier for the buyer than traditional mortgage financing—was commonplace in the US postwar era when Black households were systematically cut off from mainstream mortgage financing. Assumed to be relegated to an era of de jure segregation after the passage of fair housing legislation, contract selling has reemerged since 2008. By forensically recreating the ownership and financing histories for 515 properties in Cook County, Illinois that were sold on contract between 2011 and 2015 and a close reading of recorded land contracts, the paper connects these properties to the foreclosure crisis after 2008, and to two earlier periods of subprime lending, in the early 2000s and late 1990s. The paper uses this historical property analysis to deepen understanding of class-monopoly rent as a recursive capitalist objective within racialized property markets. In the simplest terms, class-monopoly rent is the portion of rent that owners secure through establishing and maintaining barriers to access to property. The paper describes three processes that produce racialized class-monopoly markets. First, the continued production of "group-differentiated vulnerability" to precarious housing is a premise and a product of the process of racialized uneven development. Second, the role of the state in the management of financial risk intensifies risks for communities made vulnerable by previous rounds of racial and class dispossession. Third, these two processes structure urban space so that the land contract serves as a tractable legal tool in creating and consolidating racialized class-monopoly market spaces. The result is the proliferation of racially-contingent forms of unstable housing tenure. [ABSTRACT FROM AUTHOR]

Despite the progress in pharmaceutical and epidemiological tools for combating HIV spread, HIV stigma remains a significant social barrier impeding treatment and prevention efforts, potentially reducing the effectiveness of interventions to reduce HIV transmission. In this paper, we propose a novel approach to defining and estimating HIV stigmatization through the structure of sexual relations, as opposed to attitudes. We conceptualize structural stigma as arising from two mechanisms: (1) a reduced propensity towards HIV serodiscordant partnerships (exclusion); and (2) a reduced propensity towards partnerships with seroconcordant individuals who themselves have serodiscordant partnerships (ostracism). Both mechanisms can be assessed from observed partnership network data using exponential family random graph models (ERGMs). We demonstrate our approach on a sexual contact network of black men who have sex with men in the South Side of Chicago. We find a tendency for serodiscordant sexual relationships to be suppressed in the network (θ = −0.69, p <.05), as well as a suppressive tendency for HIV negative YBMSM to have sex with other HIV negative people in serodiscordant relationships (θ = −0.96, p <.05) suggesting that structural HIV stigma is present in this network. Potential relationships with attitudinal stigma and implications for epidemiological strategies for reducing HIV stigma are discussed. • HIV stigma is present structurally in a network of men who have sex with men. • One's network position co-varies with one's beliefs about HIV transmission. • The success of HIV destigmatization campaigns can be tracked through network structure. [ABSTRACT FROM AUTHOR]

To mitigate the lack of specialty healthcare, Project ECHO (Extension for Community Health Outcomes) trains community-based primary care clinicians to better prevent the progression of, manage, and treat common health conditions. ECHO-Chicago launched in 2010 as the first urban-centered ECHO program, focusing on safety-net clinicians, and has trained over 5,175 community clinicians across 34 topic areas. This paper examines self-efficacy among ECHO-Chicago participants across 11 clinical series, including a novel use of qualitative themes from self-efficacy questions. Five years of baseline and postseries survey data were collected from 2014 to 2019, resulting in 951 participants. Paired t -tests assessed change from baseline survey to postsurvey, and Cohen's d determined effect size. Change was assessed by individual series, adult or pediatric focus, participants' prescription privilege status, and across series by qualitative question theme. Metrics included total change, any improvement, a 10% target, and a clinical competency threshold. Analysis occurred from July 2020 to January 2022. All clinical series achieved statistically significant improvement in self-efficacy, and most had a large effect size. A total of 88% had any improvement, 65% met the 10% target of 0.7 points, and 52% met the competency threshold of 5.0 in the postsurvey. Prescribers had a significantly greater increase in their self-efficacy scores than nonprescribers. With a comparison across series, each theme achieved statistical significance, with most reaching large effect sizes. ECHO-Chicago successfully increased participants' self-efficacy. This inquiry adds an urban focus, years of data, multiple series, and a novel qualitative theme component to enable comparisons across rather than solely within the ECHO series. [ABSTRACT FROM AUTHOR]

When there are significant service disruptions in public transit systems, passengers usually need guidance to find alternative paths. This paper proposes a path recommendation model to mitigate congestion during public transit disruptions. Passengers with different origins, destinations, and departure times are recommended with different paths such that the system travel time is minimized. We model the path recommendation problem as an optimal flow problem with uncertain demand information. To tackle the lack of analytical formulation of travel times due to capacity constraints, we propose a simulation-based first-order approximation to transform the original problem into a linear program. Uncertainties in demand are modeled using robust optimization to protect the path recommendation strategies against inaccurate estimates. A real-world rail disruption scenario in the Chicago Transit Authority (CTA) system is used as a case study. Results show that even without considering uncertainty, the nominal model can reduce the system travel time by 9.1% (compared to the status quo), and outperforms the benchmark capacity-based path recommendation. The average travel time of passengers in the incident line (i.e., passengers receiving recommendations) is reduced more (−20.6% compared to the status quo). After incorporating the demand uncertainty, the robust model can further reduce system travel times. The best robust model can decrease the average travel time of incident-line passengers by 2.91% compared to the nominal model. The improvement of robust models is more prominent when the actual demand pattern is close to the worst-case demand. • A transit path recommendation model for minimizing system travel times under disruptions. • Using robust optimization to consider demand uncertainty. • Propose a simulation-based first-order approximation for the non-tractable objective function. • A case study in the Chicago public transit system with a real-world disruption. • The proposed model can reduce the incident-line passengers' travel time by 20%. [ABSTRACT FROM AUTHOR]

Ride-hailing is a source of opportunity and consternation for cities, adding both expanded mobility options and added challenges such as increased congestion. In an effort to curb congestion—and to tap a new source of revenue—some US cities and states have imposed fees on ride-hail trips. Fees are far from uniform; the fee bases, amounts levied, and use of these funds once collected range considerably between cities and states. Despite previous research that transportation fee and revenue outlay structures affect equity outcomes, no research to date has examined the equity implications of ride-hail fees. This paper addresses this gap in understanding and asks: 1) what are the geographic equity implications of ride-hail fee structures; and 2) how might equity outcomes vary by how fee revenues are allocated? I answer these questions using ride-hail fee policies from 24 US cities and states, and trip-level data from over 97 million ride-hail trips taken in 2018 and 2019 in the City of Chicago. Examining trips serving low-, middle-, and high-income neighborhoods, I analyze geographic equity implications under five different fee scenarios. Variable fee structures that reflect trip factors including trip distance, pooling, location, and time of day offer the most promising geographic equity outcomes. The examined fee scenarios would raise between $30 and $122 million per year for the City of Chicago; more than three-quarters of ride-hail fee revenues are generated from trips that begin or end in high-income areas and could represent a substantial redistribution of resources from high-to low-income areas and/or travelers depending on how revenues are spent. Cities or states seeking to implement or adjust ride-hail fees should identify concrete equity goals and design fee structures and revenue streams to meet these objectives. • Ride-hail range considerably across cities and states in the US. • Five fee scenarios evaluated using 97 million ride-hail trips in Chicago. • Variable fees offer greater geographic equity outcomes vs flat fares. • Most fee revenues generated in high-income neighborhoods. • Cities should design fee structures and revenue outlays to meet equity goals. [ABSTRACT FROM AUTHOR]

• This paper examines bike diffusion behavior in a docked bike sharing system. • The analysis is inspired by an analogy between the movement of shared bikes and the spreading of epidemics among humans. • We mine a one-year bike trip data collected in a large metropolitan area in the United States. • The number of rebalancing actions that a bike receives is a strong predictor of its use and diffusion range. • The diffusion range of a bike has an upper bound well below the total number of the communities in the system. This paper examines bike diffusion behavior in a docked bike-sharing system in Chicago. The analysis is based on an analogy between the movement of shared bikes and the transmission of certain information on internet or the spreading of epidemics among humans. By mining a bike trip data set collected in the city, we find that (1) the distribution of bike trip distance peaks between 0.8 and 2 km, and beyond 6.3 km, it follows a strong power law; (2) the diffusion intensity of a community is affected positively by the number of incoming bike trips and rebalancing actions, and negatively by the percentage of inner-community trips. The effect of the rebalancing actions is roughly twice as strong as that of the incoming bike trips; (3) both the diffusion range of a bike and the number of rebalancing actions it receives are strong predictors of its use. Reaching one more community will produce about 14 more trips and an additional rebalancing action contributes about 8.6; and (4) even the most active bikes could only reach about 75% of all communities in Chicago. The last finding helps identify a cluster of communities poorly connected with the rest of the city by bike travel. Interestingly, these isolated communities are strongly correlated with the areas of the city that have high concentration of African American population, low-income households and homicide crimes. [ABSTRACT FROM AUTHOR]

• This paper defines and then quantitatively analyzes the American main street • The analysis is a quantified assessment of whether assumptions about the value of main street retail can be supported. • The paper presents a literature summary of the causes of main street decline. • A conceptual framework posits three main street qualities: density, diversity and stability. • Using proxy variables for each category, 7,000 blocks in the City of Chicago are analyzed. • The data provides support for the three qualities of main street retail. • The conclusion presents some policy options for sustaining main street retail. [ABSTRACT FROM AUTHOR]

Abstract Neighbourhood form imposes complex effects on the local microclimate. The overlook of neighbourhood microclimate will lead to miscalculation of building energy performance. In this paper, a neighbourhood-scale building simulation coupled between building energy simulation and CFD is proposed to assess the impact of external convective heat transfer coefficient (CHTC) to the building thermal performance. The co-simulation is run for different types of buildings inside the neighbourhood with different combinations of neighbourhood form parameters. The developed simulation scheme allows the iterations of neighbourhood design parameters and simulation model generation in CFD and EnergyPlus. Five empirical methods are used to predict CHTC, and the results of CHTC values, heating and cooling load intensity are compared in detail with the proposed CFD coupled method. The results suggest that the orientation and multiplier are crucial to the building load intensity in Chicago and the neighbourhood form can affect the thermal load of residential building up to about +27.1% and −18.6%, and +17.2% and −7.7% for office buildings. The proposed method and framework in this paper is capable of generating design guidelines for the optimal energy performance of the buildings in the neighbourhood. Highlights • Neighbourhood-scale building block energy simulation coupled with CFD. • Iterated various neighbourhood design parameters and building model generation. • Empirical methods in evaluating CHTCs are evaluated and compared with CFD method. • Neighbourhood form can affect certain type of building's energy use up to ± 20%. • Neighbourhood design guideline for optimal building thermal load is suggested. [ABSTRACT FROM AUTHOR]

Highlights • The Steiner Pollution-Routing Problem (SPRP) is introduced and modelled. • An exact Path Elimination Procedure (PEP) is proposed. • Instantaneous truck acceleration/deceleration rates are considered for a more accurate estimation of energy consumption. • Pareto optimal set of the SPRP is generated by implementing a mathematical programming technique. • A new model for constructing synthetic driving cycles is developed. Abstract This paper introduces the Steiner Pollution-Routing Problem (SPRP) as a realistic variant of the PRP that can take into account the real operating conditions of urban freight distribution. The SPRP is a multi-objective, time and load dependent, fleet size and mix PRP, with time windows, flexible departure times, and multi-trips on congested urban road networks, that aims at minimising three objective functions pertaining to (i) vehicle hiring cost, (ii) total amount of fuel consumed, and (iii) total makespan (duration) of the routes. The paper focuses on a key complication arising from emissions minimisation in a time and load dependent setting, corresponding to the identification of the full set of the eligible road-paths between consecutive truck visits a priori, and to tackle the issue proposes new combinatorial results leading to the development of an exact Path Elimination Procedure (PEP). A PEP-based Mixed Integer Programming model is further developed for the SPRP and embedded within an efficient mathematical programming technique to generate the full set of the non-dominated points on the Pareto frontier of the SPRP. The proposed model considers truck instantaneous Acceleration/Deceleration (A/D) rates in the fuel consumption estimation, and to address the possible lack of such data at the planning stage, a new model for the construction of reliable synthetic spatiotemporal driving cycles from available macroscopic traffic speed data is introduced. Several analyses are conducted to: (i) demonstrate the added value of the proposed approach, (ii) exhibit the trade-off between the business and environmental objectives on the Pareto front of the SPRP, (iii) show the benefits of using multiple trips, and (iv) verify the reliability of the proposed model for the generation of driving cycles. A real road network based on the Chicago's arterial streets is also used for further experimentation with the proposed PEP algorithm. [ABSTRACT FROM AUTHOR]

Borehole heat exchanger applications are growing every day – so pronouncedly that many borehole fields will likely operate in dense installation areas in the foreseeable future. In these conditions, thermal interactions will have the potential to develop between neighboring fields, with detrimental effects for the performance and efficiency of such installations in the absence of appropriate designs strategies. Currently, scarce knowledge is accessible on thermal interaction effects among borehole heat exchanger fields and a few approaches allow to effectively consider such effects in the design of these heat exchangers. This paper expands the limited competence on thermal interaction effects among borehole heat exchanger fields and presents and validates a methodology to facilitate the design of such heat exchangers. With reference to a virtual operation of multiple vertical borehole fields in the Loop district of Chicago, USA, the work highlights significant thermal interactions among neighboring borehole fields, responsible for temperature drops of up to 5.9 °C during geothermal operations lasting 50 years. The proposed methodology allows correcting the design of borehole fields to avoid overexploitation of the geothermal resource and the occurrence of litigation cases between geothermal users, representing a powerful approach to cope with thermal interaction effects among geothermal boreholes at scale. [ABSTRACT FROM AUTHOR]

This paper offers a strategic approach to Electric Vehicles (EVs) charging network planning, emphasizing the integration of demand and supply dynamics. This is accomplished through the utilization of continuous-time fluid queue models alongside discrete flow refueling location modeling, all in the context of innovation diffusion principles. Firstly, we employ a continuous-time approximation based on Ordinary Differential Equations (ODEs) to design multi-year supply curves, a method that stands in contrast to conventional practices which often overlook inter-year transitions and ongoing processes. Then, for medium-term charging station location planning (CSLP), we apply a flow refueling location model (FRLM) within grid-based multi-level networks, considering both multiple-path networks and capacity constraints. Furthermore, the grid-based network planning strategy uses a three-tier (Macro-Meso-Micro) approach for thorough EV charging station placement, with the macro-level covering entire cities, the meso -level assessing detailed EV routes and bridging the macro to micro levels, and the micro-level focusing on precise station placement for accessibility and efficiency. Lastly, our exploration of both overutilization and underutilization scenarios provides valuable insights for policymaking and conducting cost-benefit analyses. Illustrating our approach with the example of the Chicago sketch network, we introduce an integrated demand–supply model suitable for a single region and extendable to multiple regions, thereby addressing a gap in the existing literature. Our proposed methodology focuses on EV station placement, taking into account future needs, geographical capacities, and the importance of scenario analysis, which empowers strategic resource planning for EV charging networks over extended timeframes, thus aiding the transition towards a more sustainable and efficient transportation system. [ABSTRACT FROM AUTHOR]

• Gentrification precedes and follows the opening of new parks in Los Angeles. • Gentrification precedes the opening of new parks in Chicago. • Gentrification precedes and follows the opening of non-regional parks in Los Angeles. • Stronger evidence of gentrification preceding greening farther from downtown in Los Angeles. • Gentrification follows greening close to downtown but not farther away in Los Angeles. The health benefits of green space have led to calls for equitable access to parks. When new green spaces are built in low-income communities, however, gentrification often ensues. The "green gentrification" literature has paid little attention to gentrification that might occur before greening. In this paper, we explore whether and under which circumstances gentrification might precede and follow greening, a process known as the "green gentrification cycle." Focusing on Los Angeles and Chicago, we build hedonic models focused on single-family homes sold in 2010–2021 and run post-hoc pairwise comparison tests to assess price changes over time for homes within a half-mile of new parks (treatment) and farther (control). When considering all new parks, we find that gentrification is associated with subsequent greening and, to some extent, that greening is associated with ensuing gentrification in Los Angeles. In Chicago, gentrification is associated with subsequent greening, but new parks are not associated with ensuing gentrification. Models distinguishing larger regional from smaller non-regional parks show evidence of gentrification before and after non-regional parks were built in Los Angeles, but not for regional parks. In Los Angeles, we identify stronger evidence of gentrification preceding greening farther from downtown than closer to downtown, and signals of gentrification after greening near downtown but not farther away. Models focusing on new parks built near transit stations show inconclusive results. Despite methodological limitations (e.g., examining a single measure of gentrification and different park types together), our findings shed light on the complex spatiotemporal relationships between greening and gentrification. [ABSTRACT FROM AUTHOR]

• Developed a RAD generation framework to generate traffic crash characteristics. • Used a multi-layered decision process for developing a RAD dataset. • Can serve as a universal benchmark system for model frameworks in safety research. In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. The crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type – possibly establishing a universal benchmarking system for alternative model frameworks in safety literature. [ABSTRACT FROM AUTHOR]

Vaccination is one of the most important control tools to reduce Salmonella in poultry production. In order for a live vaccine to be licensed for field use it should be provided with the detection methods to differentiate it from field strains. This paper aims to describe the validation of an alternative method for the differentiation of the Salmonella 441/014 vaccine strain from field strains, using a chromogenic Media, ASAP from bioMérieux. The ASAP-based differentiation method was compared with already authorized methods, namely the Anicon SE Kylt PCR DIVA 1 assay and Ceva S-Check Salmonella differentiation kit , following the ISO 16140-6:2019 validation method guidelines. A Generalised Linear Model was fitted to the data to determine the inclusivity and exclusivity of differentiation methods (PCR Kylt vs. S-Check vs. ASAPTM). Statistical differences were based on a P-value level of < 0.05 (SPSS Inc., Chicago, IL). In this study, we show that the ASAP media was able to differentiate Salmonella Enteritidis vaccine strains from field strains, obtaining 100% agreement between the three differentiation assays. This differentiation approach is quicker, easier to deploy and cheaper as compared to alternative methods. [ABSTRACT FROM AUTHOR]

This paper analyzes and evaluates several policies aiming to mitigate the congestion effect a Transportation Network Company (TNC) brings to bear on an idealized city that contains a dense central core surrounded by a larger periphery. The TNC offers both solo and pooling e-hail services to the users of public transport. We develop a spatial market equilibrium model over two building blocks: an aggregate congestion model that describes the traffic impact of TNC operations on all travelers in the city, including private motorists, and a matching model that estimates the TNC's level of service based on the interactions between riders and TNC drivers. Based on the equilibrium model, we formulate and solve the optimal pricing problem, in which the TNC seeks to optimize its profit or social welfare subject to regulatory costs and/or constraints. Three congestion mitigation policies are implemented in this study: (i) a trip-based policy that charges a congestion fee on each solo trip starting or ending in the city center; (ii) a cordon-based policy that charges TNC vehicles entering the city center with zero or one passenger; and (iii) a cruising cap policy that requires the TNC to maintain the fleet utilization ratio in the city center above a threshold. Based on a case study of Chicago, we find TNC operations may have a significant congestion effect. Failing to anticipate this effect in the pricing problem leads to sub-optimal decisions that worsen traffic congestion and hurt the TNC's profitability. Of the three policies, the trip-based policy delivers the best performance. It reduces traffic congestion modestly, keeps the TNC's level of service almost intact, and improves overall social welfare substantially. The cruising cap policy benefits private motorists, thanks to the extra congestion relief it brings about. However, because other stakeholders together suffer a much greater loss, its net impact on social welfare is negative. Paradoxically, the policy could worsen the very traffic conditions in the city center that it is designed to improve. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of rail transit and taxi by travel cost and time based on the large-scale taxi trip data and public transit schedule information in two major U.S. cities. To quantify the relative advantage of one mode over the other, we introduce the notion of travel gradient, which is travel cost difference divided by travel time difference. Based on the signs of travel cost and time differences, we classify all trips into four quadrants. Quadrant II trips are selected for further analysis because rail transit is identified to be competitive with taxi for such trips. We also explore the relation between various trip characteristics and travel gradient with and without considering the spatial variation of such a relation. Main research findings include: (1) around 70% of the taxi trips in the considered datasets can be substituted with rail transit trips if the maximum walking distance is 0.5 miles at each trip end; (2) for around 10% of taxi trips with both modes being viable, rail transit dominates taxi in both travel cost and time; for the rest, rail transit is competitive with taxi; (3) the marginal travel cost saving due to mode switching from taxi to rail transit is about $70; and (4) there exist clearly spatial variations of the relation between trip characteristics and travel gradient. The main policy recommendation from this study is that rail transit can be better marketed by highlighting its relative advantage over taxi in travel time and cost, especially for travels in certain directions and time periods. • The comparative advantage of rail transit over taxi is evaluated with data in Washington D.C. and Chicago. • Around 70% of the taxi trips can be substituted by rail transit with a maximum walking distance of 0.5 miles. • Rail transit dominates taxi for around 10% of the taxi trips, while taxi never dominates rail. • On average, the marginal travel cost saving due to mode switching from taxi to rail transit is about $70. • Targeted rail transit marketing should be launched based on the geographically weighted regression results. [ABSTRACT FROM AUTHOR]

The rapid popularity growth of shared e-scooters creates the necessity of understanding the determinants of shared e-scooter usage. This paper estimates the impacts of temporal variables (weather data, weekday/weekend, and gasoline prices) and time-invariant variables (socio-demographic, built environment, and neighborhood characteristics) on the shared e-scooter demand by using four months (June 2019- October 2019) period of data from the shared e-scooter pilot program in Chicago. The study employs a random-effects negative binomial (RENB) model that effectively models shared e-scooter trip origin and destination count data with over-dispersion while capturing serial autocorrelation in the data. Results of temporal variables indicate that shared e-scooter demand is higher on days when the average temperature is higher, wind speed is lower, there is less precipitation (rain), weekly gasoline prices are higher, and during the weekend. Results related to time-invariant variables indicate that densely populated areas with higher median income, mixed land use, more parks and open spaces, public bike-sharing stations, higher parking rates, and fewer crime rates generate a higher number of e-scooter trips. Moreover, census tracts with a higher number of zero-car households and workers commuting by public transit generate more shared e-scooter trips. On the other hand, results reveal mixed relationships between shared e-scooter demand and public transportation supply variables. This study's findings will help planners and policymakers make decisions and policies related to shared e-scooter services. [ABSTRACT FROM AUTHOR]

Scientific publications, specifically journal papers, are facing increasing challenges in a number of ways for both authors and publishers, and one of them is the friendliness of the format requirements by different journals even under the same editorial management system of the same publisher. At our most recent editors' meeting of this journal, decision were made and agreed upon for the following three items: 1) requirements on the physical form and style of manuscript will be relaxed immediately on the presentation of Results and Discussion, so that either combined or separation of them into 2 different sections are accepted for further processing; 2) citations of references in text will not be mandatorily to any specific one, so citations using Harvard or Chicago styles will be both acceptable from now on; and 3) a new category of publication will be started for a testing on experimental stage as Fast-track results. With all these new changes, it is hoped that publication will be less time consuming for authors so that precious time can be used in research than formatting or re-formatting the style for a submission. [ABSTRACT FROM AUTHOR]

Urban energy demand aggregation (UEDA) is a key aspect of urban sustainability, as it can help to improve the energy efficiency of urban systems and reduce their environmental impacts. However, UEDA is a challenging task, as it involves aggregating heterogeneous and diverse energy demands of individual buildings into a collective demand at a given spatial scale. This paper proposes a novel entropy-based method for UEDA that quantifies the information loss or distortion resulting from this aggregation process. The method also identifies the optimal spatial scale for UEDA that minimizes information loss or distortion, and evaluates the quality and reliability of UEDA results using entropy-based metrics. We apply the method to a case study of Chicago, where we estimate and analyze the energy demand of buildings at 10 spatial scales, ranging from 1.5 km to 15 km, and for different types of energy sources. We calculate the entropy for each spatial scale and energy source, and compare it with building characteristics and ZIP codes. We also assess the quality and reliability of UEDA results using entropy-based metrics, such as information gain ratio and normalized mutual information. Our results show that different spatial scales reveal different patterns and relationships of energy demand, and that choosing an appropriate scale can enhance the accuracy and efficiency of UEDA. Our results also show that there is an optimal spatial scale for UEDA that balances information preservation and reduction, and that this scale may vary depending on the type of energy source and the urban context. Our findings contribute to the field of UEDA and urban sustainability by developing a novel perspective on urban energy dynamics, revealing the complexity and diversity of urban systems, such as population, land use, transportation, and energy demand. • Introduced an entropy-based approach for urban energy demand analysis. • Optimized spatial scales in energy planning for urban sustainability. • Examined energy consumption patterns across diverse urban scales. • Explored policy implications for efficient urban energy management. [ABSTRACT FROM AUTHOR]

As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as "shoeboxes" using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning. • Optimization framework combines urban building energy modeling and local planning of renewables. • Urban neighborhood buildings are modeled in an abstracted manner as "shoeboxes". • Considering local wind and solar resources along with energy storage and grid supply. • PV has the most prominent role in supplying local renewables to the neighborhood. • Substantial reduction in CO2 emissions (75%) can be achieved with limited increase in energy costs (15%). [ABSTRACT FROM AUTHOR]

Urban streets, especially curbside spaces, are increasingly complex and often contested. The Coronavirus pandemic created a new demand for street space to enable physical distancing. The urgency of the pandemic and the fear of covid exposure suspended traditional community engagement opportunities, opposition from residents and business owners, and considerations over the privatization of public street space. This exploratory paper uses the case studies of Toronto and Chicago to trace the past and present regulations and programs affecting curbside parking spaces. Through these cases, this paper addresses larger questions that relate the historical conceptions around curbside parking use with those put forward in response to the pandemic. Given the popularity of pandemic-related curbside space programs and their potential to become long-term interventions, this paper also raises several questions around privatization, access and social equity that must be addressed in future iterations of programs affecting curbside space. • Cities are rapidly reimagining curbside space as a result of the pandemic. • Cities are reducing barriers to accessing curbside parking space by eateries. • Shifts in curb space use raise questions around privatization, access and social equity. • Long-term curb space interventions need to be more equitable and public. [ABSTRACT FROM AUTHOR]

This paper develops an adaptive multi-model fusion approach to predict building energy consumption, aiming to give useful suggestions for better energy control. The building energy benchmarking dataset of Chicago in 2017 is selected as the case study, where 9 features are selected as the input variables aiming to estimate the weather normalized site energy use intensity of buildings. The training dataset is clustered using the K-means algorithm and sub-models are trained based on the clustered data using the XGBoost algorithm. The sub-models are then fused by assigning a weight considering both the model reliability and the matching degree and adopting a screening algorithm to weed out the unmatching sub-models, where the influence of the threshold in the screening algorithm is studied. The root mean square error of the estimation results from a fused model is found to be 13.42 which achieves a 7.6% amelioration compared with a single model. Moreover, the adaptive multi-model fusion approach is also proved to outperform both the two-stage clustering-based regression method and the linear fusion method. Benefiting from proper treatment of samples in the fuzzy zones between clusters and the screening algorithm in the fusion process, the method proposed in our paper eventually serves as more advanced guidance in the analysis and control of building energy performance. • An adaptive multi-model fusion approach with clustering and ensemble learning is proposed • The building energy benchmarking dataset of Chicago in 2017 is selected as the case study. • A comparison is made between estimation with a single and a fused model. • Higher accuracy and stability of estimation with a fused model is achieved. • The sub-model screening algorithm considerably improves the model performance. [ABSTRACT FROM AUTHOR]

Machine learning and artificial intelligence (ML/AI), previously considered black box approaches, are becoming more interpretable, as a result of the recent advances in eXplainable AI (XAI). In particular, local interpretation methods such as SHAP (SHapley Additive exPlanations) offer the opportunity to flexibly model, interpret and visualise complex geographical phenomena and processes. In this paper, we use SHAP to interpret XGBoost (eXtreme Gradient Boosting) as an example to demonstrate how to extract spatial effects from machine learning models. We conduct simulation experiments that compare SHAP-explained XGBoost to Spatial Lag Model (SLM) and Multi-scale Geographically Weighted Regression (MGWR) at the parameter level. Results show that XGBoost estimates similar spatial effects as those in SLM and MGWR models. An empirical example of Chicago ride-hailing modelling is presented to demonstrate the utility of SHAP with real datasets. Examples and evidence in this paper suggest that locally interpreted machine learning models are good alternatives to spatial statistical models and perform better when complex spatial and non-spatial effects (e.g. non-linearities, interactions) co-exist and are unknown. • Local interpretable methods (e.g. SHAP) enable the extraction of spatial effects from machine learning model of spatial data. • Simulation results suggest that spatial effects modelled by XGBoost are similar to the parameters in SLM and MGWR. • An example of ride-hailing demand in Chicago is presented to demonstrate the use of local interpretable machine learning. • This research provides evidence to support and improve the use of machine learning in spatial data modelling. [ABSTRACT FROM AUTHOR]

• The residential attainment experiences of resettled refugees diverge shortly after arrival. • Housing trajectories of resettled refugees reflect a reconstruction of prior lives. • Pre-arrival experiences shape the housing preferences of resettled refugees. • Findings challenge underlying logic of residential attainment theories. • Distribution policy of resettlement agencies contributes to secondary migration. This paper uses the initial resettlement conditions of refugees in Chicago as a near natural experiment to study how housing choices are made and actualized. Sixty-two refugees from three different ethnocultural groups (Muslim Iraqis, Karen Burmese and Lhotsampa Bhutanese) are surveyed after five years to analyze their housing movements and the reasons behind those movements. Using mixed methods, but relying primarily on individual interviews and census data, this research compares the trajectories of those three groups. Analyses indicate that the three groups follow very different residential pathways post arrival despite initially being resettled in the same census tracts. The discrepancy can be explained by groups making decisions concerning housing location and type based primarily on imported preferences, using resources accrued prior to arrival, within the context of reception (neighborhood and housing qualities). Looked at separately, each group superficially provides support for a different theory of residential attainment. However, when the individual is asked, the primary motivation for their decisions is an attempt to recreate aspects of their prior lives indicating that pre-arrival characteristics should be central to future theories of residential attainment. [ABSTRACT FROM AUTHOR]

• Investigates behavioral explanations for the bicycle safety-in-numbers effect. • Data on 79 speed cameras locations in the City of Chicago, 2014–2016. • 26 treatment locations saw increased public bike share traffic. • Motor-vehicle speed violations fell at these locations relative to a control group. • Estimated elasticity between rental bike usage and speed violations is −0.04. Introduction : This paper investigates whether motor-vehicle driver behavior changes when there are more bicycles on the road. Method : Data on trips on a rapidly expanding public bike share scheme in Chicago are combined with speed violations captured by a network of 79 cameras. Using weekly data from July 2014 to December 2016, violations at 26 sites where there was a considerable increase in bicycle traffic are compared with a control group of 53 locations where rental bicycles are not available. Results : An increase in rental bicycle usage is statistically related to a reduction in the number of speeding violations, with an estimated elasticity of −0.04. Conclusion : The increased presence of bicyclists makes at least some motorists drive more cautiously. Practical Application : This research provides some insight into the mechanism behind the observed reduction in crash rates as bicyclists become more numerous. Some motorists moderate their speeds allowing more time to avoid collisions and a reduction in the severity of the vehicle-bicyclist collisions that still occur. [ABSTRACT FROM AUTHOR]

This paper proposes a new measure of wind power variability and investigates the impacts of wind power variability on the optimal sizing of Standalone Wind Power (SWP) systems. The proposed new measure of the wind power variability in the frequency domain, which mainly includes a cumulative energy distribution index and a fluctuation factor, is applied to assess the variability of wind power throughout 6 consecutive years from 6 far apart sites from latitude 0°–50° across America. Big data assessment results indicate the intermittent wind power at one site can be treated as Quasi-Time-Invariant (QTI) in the frequency domain. Big data simulations of the six SWP systems with the same residential load demand at the six sites provide QTI responses of the power supply reliability against the sizing of the system components in the mitigation of wind power variability. A case study of optimal sizing of a SWP system at Chicago, was carried out, which aims to minimize the system cost while satisfying the requirement of power supply reliability. It can be found from the study that, the proposed approach provides a new way to significantly reduce the computation in the optimal sizing of SWP systems. • A new measure of wind power variability is proposed in the frequency domain. • The wind power variability is quasi-time-invariant (QTI) in the frequency domain. • QTI analysis results can be used for simplifying the optimal sizing of SWP systems. [ABSTRACT FROM AUTHOR]

• Exploring network partitioning impacts on the reliability relation in large networks. • Proposing a modified dynamic methodology to partition a large-scale network. • Comparing two proposed approaches in literature to define the reliability relation. • Results show that sub-trajectory method is more consistent with network partitioning. • Positive impacts of partitioning on the reliability parameter estimation for clusters. Network travel time reliability can be represented by a relationship between network space-mean travel time and the standard deviation of network travel time. The primary objective of this paper is to improve estimation of the network travel time reliability with network partitioning. We partition a heterogeneous large-scale network into homogeneous regions (clusters) with well-defined Network Fundamental Diagrams (NFD) using directional and non-directional partitioning approaches. To estimate the network travel time reliability, a linear relationship is estimated that relates the mean travel time with the standard deviation of travel time per unit of distance at the network level. The impact of different partitioning approaches, as well as the number of clusters, on the network travel time reliability relationship are also explored. To estimate individual vehicle travel times, we use two distinct approaches to allocate vehicle trajectories to different time intervals, namely trajectory and sub-trajectory methods. We apply the proposed framework to a large-scale network of Chicago using a 24-h dynamic traffic simulation. Partitioning and travel time reliability estimation are conducted for both morning and afternoon peak periods to demonstrate the impacts of travel demand pattern variations. The numerical results show that the sub-trajectory method for the network travel time reliability estimation and the directional partitioning with three clusters have the highest performance among other tested methods. The analyses also demonstrate that partitioning a heterogeneous network into homogeneous clusters may improve network travel reliability estimation by estimating an independent relationship for each cluster. Also, comparing morning and afternoon peak periods suggests that the estimated parameter for the linear network travel time reliability relationship is directly related to the coefficient of variation of density as a measure of spatial distribution of congestion across the network. [ABSTRACT FROM AUTHOR]

Abstract: This paper calculates the cross elasticity between the price of gasoline and transit ridership in Chicago using monthly data for the period between January 1999 and December 2010. Separate estimations are conducted for city heavy rail, city bus, commuter rail and suburban bus services. A 12-month difference model is used to overcome seasonality. The paper finds that the cross elasticities when gas prices were less than $3 a gallon were small, with a magnitude of less than 0.05. When prices exceeded $3 a gallon, the elasticity was larger, in the range of 0.12–0.14, for the rail modes. In the summer of 2008 when prices exceeded $4 a gallon, there was considerable responsiveness with elasticities of 0.28–0.30 for city and suburban bus, and 0.37 for commuter rail. These values are similar to, or even larger than, those found during the oil crises of the 1970s and early 1980s. [Copyright &y& Elsevier]

Abstract: Using the data on all live births (∼400,000) and criteria pollutants from the Chicago Metropolitan Statistical Area (MSA) between 2000 and 2004, this paper empirically demonstrates how mismatches in the spatiotemporal scales of health and air pollution data can result in inconsistency and uncertainty in the linkages between air pollution and birth outcomes. This paper suggests that the risks of low birth weight associated with air pollution exposure changes significantly as the distance interval (around the monitoring stations) used for exposure estimation changes. For example, when the analysis was restricted within 3 miles distance of the monitoring stations the odds of LBW (births <2500 g) increased by a factor of 1.045 (±0.0285 95% CI) with a unit increase in the average daily exposure to PM10 (in μg m−3) during the gestation period; the value dropped to 1.028 when the analysis was restricted within 6 miles distance of air pollution monitoring stations. The effect of PM10 exposure on LBW became null when controlled for confounders. But PM2.5 exposure showed a significant association with low birth weight when controlled for confounders. These results must be interpreted with caution, because the distance to monitoring station does not influence the risks of adverse birth outcomes, but uncertainty in exposure increases with the increase in distance from the monitoring stations, especially for coarse particles such as PM10 that settle with gravity within short distance and time interval. The results of this paper have important implications for the research design of environmental epidemiological studies, and the way air pollution (and potentially other environmental) and health data are collocated to compute exposure. While this paper challenges the findings of pervious epidemiological studies that have relied on coarse resolution air pollution data (such as county level aggregated data), the paper also calls for time-space resolved estimate of air pollution to minimize uncertainty in exposure estimation. [Copyright &y& Elsevier]

• Substantial heterogeneity in time use preferences for different activities was found. • Travellers are more likely to socialize or talk when using ride-hailing services. • Preferences for multitasking can largely be explained by trip-specific attributes. • Traveling alone for at least 20 min can free up time later in the day due to multitasking. This paper presents an empirical analysis of a survey among public transit users in the Chicago metropolitan area. The survey collected information about travelers' general preferences for travel-based multi-tasking during public transit use, revealed preferences for travel-based multi-tasking during a reference trip by public transit in the Chicago metropolitan area and general preferences for travel-based multitasking during the use of ride-hailing services. To analyze the preference data, a rank-ordered logit models is estimated. The findings show that preferences for performing different productive and non-productive tasks while traveling can largely be explained by individual-specific attributes such as age and marital status as well as by trip-specific attributes such as the trip duration and travel party composition. In addition, the survey requested information about the usefulness of travel-based multitasking during the reference trip. Individuals traveling by themselves and for at least twenty minutes are found to be comparatively more likely to have been able to free up time later in the day, as they were able to multitask during the reference trip. [ABSTRACT FROM AUTHOR]

• We analyze the resilience of weighted networks. • We propose a measure of resilience based on shock propagation along network paths. • We introduce a condition for shock propagation based on the weights of the network. • We test the theoretical proposal on two real-world instances of air traffic systems. Networks are at the core of modeling many engineering contexts, mainly in the case of infrastructures and communication systems. The resilience of a network, which is the property of the system capable of absorbing external shocks, is then of paramount relevance in the applications. This paper deals with this topic by advancing a theoretical proposal for measuring the resilience of a network. The proposal is based on the study of the shocks propagation along the patterns of connections among nodes. The theoretical model is tested on the real-world instances of two important airport systems in the US air traffic network: Illinois (including the hub of Chicago) and New York states (with JFK airport). [ABSTRACT FROM AUTHOR]

Highlights • A 30 × 30 m coupled model of economics, land-use, and ecosystem services is constructed. • A dynamic and spatially explicit ecosystem service index is developed. • Reference scenario is compared to an ecosystem preservation district scenario. • Ecosystem district policy is a critical tool for mitigating ecosystem impacts. • Strategies for policy scenario analysis and stakeholder engagement is proposed. Abstract A comprehensive understanding and modeling of socio-ecological systems can better assess how human-induced urban growth affects ecosystem service. This paper demonstrates a comprehensive socio-ecological system modeling approach to identify ecosystem service impacts that result from human activities within large, complex urban systems. It adds to the existing ecosystem impact assessment approaches by integrating spatiotemporal and socio-ecological process model with dynamic interaction and feedback between models and policies. The approach couples regional economics, urban land-use, and ecosystem interactions in a fine scaled (30 × 30 m) modeling framework with multiple time steps and feedback. The coupled model fuses a regional econometric input-output model with a dynamic land-use allocation model that spatially allocates the residential and commercial lands that are needed to satisfy the identified economic changes on an annual basis. A dynamic and spatially explicit index is constructed to measure the potential impacts of the economic/land use interactions on available ecosystem services. A scenario analysis is performed to test various ecologically oriented policy outcomes against noted ecosystem service impacts. The Chicago metropolitan region constitutes the study area with forecasts to 2040. Analysis results prove that models without inclusion of feedback dynamics and uncertainties could significantly under-estimate resulting ecosystem service impacts without. The scenario simulation shows a significant land-use developmental pattern restructure occurs when preservative policies are reinforced and significant ecosystem service loss reduction can also be achieved. [ABSTRACT FROM AUTHOR]

This paper focuses on Australian feminist novelist Stella Miles Franklin's unpublished one-act play 'The Waiter Speaks', previously unremarked by Franklin scholars. Written during Franklin's work as secretary for the National Women's Trade Union League, the play addresses the 1910-1911 Chicago garment workers' strike, a massive conflict that shaped union history through its precedent for arbitration and a female leadership that crossed class and ethnic lines. Franklin was in charge of League publicity for the strike and provided commentary on the conflict through the League's journal Life and Labor. Such experiences provided narrative authority for her protest play that sought to educate and provide a 'call to arms'. The paper contributes to U.S. women's history and Australian literary studies by giving voice to this long- forgotten play of a notable feminist author, contextualising it in labour history and in the literary traditions that shaped Franklin's writing during this period. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we present an analysis of the relationship between pedestrian–vehicle crashes and characteristics of areas with high low-income and minority populations in the Chicago metropolitan area (also called environmental justice or EJ areas in the United States). While related research has indicated that pedestrian crashes occur more frequently in these areas than in non-EJ areas, this paper attempts to relate the incidence to environmental characteristics and behavioral factors through a better understanding of the contributing factors present in crash occurrences in EJ versus non-EJ areas. Specially constructed small-area factors from a Spatial Decision Support System (SDSS) are used to explain pedestrian–vehicle crashes. Using a Poisson model that corrects for underreporting, we find that pedestrian crash incidents in EJ areas are related to variables of exposure (including the suitability of the area for walking and transit accessibility), crime rates, transit availability, and general population demographics such as income and presence of children. Results suggest that it may be necessary to better incorporate a safety perspective or measures of safety improvements in pedestrian and transit improvements and expansion programs within EJ areas. [ABSTRACT FROM AUTHOR]

This paper examines residential mobility patterns of households living in low-income neighborhoods, paying close attention to the neighborhood characteristics that influence their mobility. Using several million records of household-level datasets for address change trajectories in the Chicago metropolitan area, this paper explores the relationship among movers and the associated neighborhood-level income differences between origin and destination locations, to interpret which households from low-income neighborhoods can use residential mobility to move up to more neighborhood conditions result in residential mobility outcomes to favorable neighborhood conditions and potentially end the cycle of poverty. This research finds the positive prospects for low-income neighborhood households to move up, while the characteristics (e.g., racial composition) of origin and destination neighborhoods play a significant role in relocation outcomes. In addition, housing mobility programs can have various impacts on households moving from neighborhoods of different income levels. Policymakers should be aware of the diverse needs of households in extremely low-income neighborhoods (less concentrated housing assistance) and in very low-income neighborhoods (public housing) that can help them move out of concentrated poverty and move up to neighborhoods with higher income spectrums. • Mobility outcome factors of households from low-income neighborhoods are examined. • Data includes one-decade address changes with over 38-million entries in Chicago. • A significant portion of residents in lowest-income neighborhoods moved to higher income communities during the study period. • A lower none-white population is associated with favorable relocation outcomes. • Public housing in destination does not necessarily reproduce concentrated poverty. [ABSTRACT FROM AUTHOR]

Throughout the United States, suburbs have seen an increasing number of immigrant populations as residents in the area. Yet, the suburban social and health services available have not developed to serve these immigrant groups, leading to community-based organizations (CBOs) filling in the need for service provision. However, the geographic distribution and resources available to these CBOs to serve these groups is uneven and understanding the constraints these organizations face in delivering services is crucial. Employees in CBOs themselves have crucial insights into immigrants' needs and concerns, particularly those of immigrant women. This paper examines the understanding of CBO staffers focusing on the Latine population in Chicago and the suburbs, particularly on healthcare access barriers CBOs provide interventions on for Latina women during the COVID-19 pandemic. Qualitative data from a purposeful sample of interviews with CBO staffers during the COVID-19 pandemic supports the findings of financial and legal barriers, particularly access to information, and creative, community-based methods for interventions, specific to the experiences of Latina women. [ABSTRACT FROM AUTHOR]

Developing an accurate and robust prediction of long-term average global solar irradiation plays a crucial role in industries such as renewable energy, agribusiness, and hydrology. However, forecasting solar radiation with a high level of precision is historically challenging due to the nature of this source of energy. Challenges may be due to the location constraints, stochastic atmospheric parameters, and discrete sequential data. This paper reports on a new hybrid deep residual learning and gated long short-term memory recurrent network boosted by a differential covariance matrix adaptation evolution strategy (ADCMA) to forecast solar radiation one hour-ahead. The efficiency of the proposed hybrid model was enriched using an adaptive multivariate empirical mode decomposition (MEMD) algorithm and 1+1EA-Nelder–Mead simplex search algorithm. To compare the performance of the hybrid model to previous models, a comprehensive comparative deep learning framework was developed consisting of five modern machine learning algorithms, three stacked recurrent neural networks, 13 hybrid convolutional (CNN) recurrent deep learning models, and five evolutionary CNN recurrent models. The developed forecasting model was trained and validated using real meteorological and Shortwave Radiation (SRAD1) data from an installed offshore buoy station located in Lake Michigan, Chicago, United States, supported by the National Data Buoy Centre (NDBC). As a part of pre-processing, we applied an autoencoder to detect the outliers in improving the accuracy of solar radiation prediction. The experimental results demonstrate that, firstly, the hybrid deep residual learning model performed best compared with other machine learning and hybrid deep learning methods. Secondly, a cooperative architecture of gated recurrent units (GRU) and long short-term memory (LSTM) recurrent models can enhance the performance of Xception and ResNet. Finally, using an effective evolutionary hyper-parameters tuner (ADCMA) reinforces the prediction accuracy of solar radiation. • A new hybrid deep residual learning with gated LSTM is proposed for solar radiation. • Cooperative architecture of GRU and LSTM enhanced Xception and ResNet performance. • Adaptive evolutionary multivariate empirical mode decomposition method is introduced. • Effective differential covariance matrix adaptation strategy proposed for tuning model. • The proposed hybrid model outperformed 13 hybrid and popular prediction models. [ABSTRACT FROM AUTHOR]

Recent evidence suggests that collecting and tracking patient-reported outcomes (PROs) are correlated with improved patient quality of life, patient satisfaction and physician-patient communication. With the ongoing shift in the health care industry to value-based care, more emphasis is placed on patient-reported measures as well as clinical outcomes and cost reduction methods. Typically, PROs are collected via paper forms, but this method has been shown to be difficult to incorporate and a burden on clinical staff and patients. The literature is scarce regarding the value of using electronic Patient Reported Outcome (ePRO) systems in nonclinical trial settings, and much of the existing literature focuses on phone applications that require installation. We sought to assess the usability of an automated app-less SMS-based electronic tool to collect spine PROs. To assess the feasibility of implementing an automated SMS-based electronic Patient Reported Outcome (ePRO) tool for clinical outcome assessment in spinal surgery patients. Patients receiving the SMS HIPAA-compliant survey link were prospectively enrolled in the ePRO tool's administrative web portal from June 1, 2020, to October 1, 2020. Outcome data for patients receiving either the SMS or paper survey was retrospectively analyzed. A total of 204 patients were included in this study. Group1 included 88 patients who underwent either a microdiscectomy (9), lumbar fusion (42), or anterior cervical fusion (37) from June 1, 2020, to October 1, 2020, and were sent an outcome survey via a SMS HIPAA-compliant link. Group 2 included 116 patients who received either a microdiscectomy (14), lumbar fusion (69) or anterior cervical fusion (34) from November 1, 2019 to March 1, 2020, and were administered an in-office paper outcome survey. Two outcomes were investigated in this study: survey compliance rate (response rate) and total mean time required for patient outcome assessment (includes mean survey response time and mean staff administration time). A total of 88 patients undergoing microdiscectomies (9), lumbar fusions (42), and anterior cervical fusions (37) from June 1, 2020, to October 1, 2020 were prospectively enrolled in the tool's administrative web portal. Patients received automated outcome assessment surveys via a HIPAA-compliant SMS link at 1 week, 1 month, and 3 months post-surgery. Once the patient clicked on the SMS link, they were led to a secure web browser in which they were able to complete the survey. All survey responses were captured in a secure web portal which allowed physicians and associated staff to view responses. Prior to the application of the ePRO tool, outcome information was collected at the aforementioned timepoints via paper for 116 patients who received microdiscectomies (14), lumbar fusions (69) and anterior cervical fusions (34) from November 1, 2019 to March 1, 2020. In this study, compliance rate and the mean time required for outcome assessment were compared between patients receiving SMS surveys and patients receiving paper surveys. Both groups of patients (SMS vs paper) had analogous demographic, medical history, and surgical characteristics. Data analysis was conducted in SPSS 20.0 (SPSS, Inc. Chicago, IL) and included one-way ANOVAs and two-tailed t-tests. The PRO data capture rate was 52% with SMS surveys (146/279) and 26% (94/356) with paper surveys (p < 0.05). The total mean time associated with capturing outcomes via SMS surveys (19 hours) was one-third of the time associated with the paper survey method (57 hours) (p < 0.05). Use of the ePRO tool resulted in a reduction in both the mean survey completion time (SMS: 5 minutes, paper: 7.5 minutes) and mean administration time spent by staff on each patient (SMS: 5 minutes, Paper: 17 minutes). Minimal time effort was required from staff after initial patient enrollment. A survey assessing usability was administered to clinical staff (n=10) and patients (n=88); all staff and patients indicated a preference for use of the ePRO tool compared to the in-office paper survey. Initial results indicate that implementation of an app-less SMS-based ePRO tool in a spine practice setting is feasible and mutually beneficial to patients and providers. SMS survey administration of PROs for spine surgery patients increased the capture rate of clinical outcomes, allowed for data collection in the absence of an office visit, and decreased the total mean time associated with PRO collection. The ePRO tool allowed for large-scale and timely data collection, and we believe use of such a tool can potentially facilitate early detection of patient issues and complications. Future studies would benefit by examining the value of using such a tool to identify patient complications in a timely manner. This abstract does not discuss or include any applicable devices or drugs. [ABSTRACT FROM AUTHOR]

Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and managing modern transportation systems. Existing studies develop statistical models for ride-sourcing demand estimation at an aggregate level due to limited data availability. These models lack foundations in microeconomic theory, ignore competition of ride-sourcing with other travel modes, and cannot be seamlessly integrated into existing individual-level (disaggregate) activity-based models to evaluate system-level impacts of ride-sourcing services. In this paper, we present and apply an approach for estimating ride-sourcing demand at a disaggregate level using discrete choice models and multiple data sources. We first construct a sample of trip-based mode choices in Chicago, USA by enriching household travel survey with publicly available ride-sourcing and taxi trip records. We then formulate a multivariate extreme value-based discrete choice model with sampling and endogeneity corrections to account for the construction of the estimation sample from multiple data sources and endogeneity biases arising from supply-side constraints and surge pricing mechanisms in ride-sourcing systems. Our analysis of the constructed dataset reveals insights into the influence of various socio-economic, land use and built environment features on ride-sourcing demand. We also derive elasticities of ride-sourcing demand relative to travel cost and time. Finally, we illustrate how the developed model can be employed to quantify the welfare implications of ride-sourcing policies and regulations such as terminating certain types of services and introducing ride-sourcing taxes. • Data fusion approach for ride-sourcing demand estimation. • Address endogeneity arising from characteristics of ride-sourcing systems. • MEV-based discrete choice model with sampling and endogeneity corrections. • Explain ride-sourcing demand, derive elasticities. • Quantify welfare implications of ride-sourcing policies and regulations. [ABSTRACT FROM AUTHOR]

Ridesplitting – a type of ride-hailing in which riders share vehicles with other riders – has become a common travel mode in some major cities. This type of shared ride option is currently provided by transportation network companies (TNCs) such as Uber, Lyft, and Via and has attracted increasing numbers of users, particularly before the COVID-19 pandemic. Previous findings have suggested ridesplitting can lower travel costs and even lessen congestion by reducing the number of vehicles needed to move people. Recent studies have also posited that ridesplitting should experience positive feedback mechanisms in which the quality of the service would improve with the number of users. Specifically, these systems should benefit from economies of scale and increasing returns to scale. This paper demonstrates evidence of their existence using trip data reported by TNCs to the City of Chicago between January and September 2019. Specifically, it shows that increases in the number of riders requesting or authorizing shared trips during a given time period is associated with shorter trip detours, higher rates of riders being matched together, lower costs relative to non-shared trips, and higher willingness for riders to share trips. [ABSTRACT FROM AUTHOR]

• This paper inferred the impact of COVID-19 on transit ridership. • COVID-19 pandemic led to an average 72.4% drop in ridership for 95% of stations in Chicago. • Ridership declined more in zones with more commercial lands. • Ridership declined more in zones with higher percentages of white, educated, and high-income individuals. • Ridership declined less in zones with more "essential" jobs. The COVID-19 pandemic has led to a globally unprecedented decline in transit ridership. This paper leveraged the 20-years daily transit ridership data in Chicago to infer the impact of COVID-19 on ridership using the Bayesian structural time series model, controlling confounding effects of trend, seasonality, holiday, and weather. A partial least square regression was then employed to examine the relationships between the impact of ridership and various explanatory factors. Results suggested: (1) COVID-19 pandemic exerted significant effects on 95% of transit stations, leading to an average 72.4% drop in ridership. (2) Ridership declined more in regions with more commercial lands and higher percentages of white, educated, and high-income individuals. (3) Regions with more jobs in trade, transportation, and utility sectors presented smaller declines. (4) Regions with more COVID-19 cases/deaths presented smaller declines in transit ridership. Findings provide a timely understanding of the significantly reduced ridership during the pandemic and help transit agencies adjust services across different socioeconomic groups and space to better constrain virus transmission. [ABSTRACT FROM AUTHOR]

Where is public money for place-based improvement spent? A common narrative is that public expenditure prioritizes business and development interests at the expense of disadvantaged areas that have the greatest need. This paper is a quantified look at this question—an analysis of spending patterns at the neighborhood level. The spatial analysis of public expenditure at the neighborhood level is surprisingly rare, in part because the data is not conducive to locational assessment at a detailed level. We posit three explanations for the pattern of expenditure: efficiency, pro-development, or equity/need. We find that place-based public expenditure in Chicago is unrelated to efficiency or equity/need, and mostly follows a pattern of spatial selectivity in which business and economic development are prioritized, primarily in and around the central business district and higher income residential areas. Our study augments earlier findings in that funding is considered in a spatially explicit manner at a highly disaggregated level of analysis. • A common narrative is that public expenditure prioritizes business and development interests at the expense of disadvantaged areas that have the greatest need. • This paper is a quantified look at spending patterns at the neighborhood level. • This level of expenditure analysis rare, as most often, data is not conducive to locational assessment at a detailed level. • We posit three explanations for the pattern of expenditure: efficiency, pro-development, or equity/need. • We find that place-based public expenditure in Chicago is unrelated to efficiency or equity/need, and mostly follows a pattern of spatial selectivity in which business and economic development are prioritized. [ABSTRACT FROM AUTHOR]

• Plant species show spatial patterns within and across neighborhoods. • Spatial autocorrelation was observed in cultivated and spontaneous species. • Cultivated plant species have high turnover and uniqueness across yards. • College education partly explained differences in neighborhood plant diversity. Residential yards and gardens can have surprisingly high plant diversity. However, we still do not understand all the factors that drive diversity in individual gardens, or how gardens scale up to create larger patterns of urban biodiversity. For example, social interactions between neighbors could affect whether they mimic each other's yard design, affecting spatial turnover in plant communities. Further, socio-economic differences between neighborhoods might result in distinct plant assemblages across a city. In this paper, we used fieldwork, GIS, and spatial statistics to examine the variability in front yard vegetation—both cultivated and spontaneous plants—in 870 yards in Chicago, Illinois (USA). Our goals were to understand diversity and spatial patterning of plant communities in residential neighborhoods and how they vary with scale, considering alpha, beta, and gamma diversity. We addressed the following questions: (1) How do alpha, beta, and gamma diversity of cultivated and spontaneous plants vary between neighborhoods with different socioeconomic characteristics? (2) Within neighborhoods, do we see spatial autocorrelation in front-yard plant communities? If so, do those spatial patterns affect plant diversity at the neighborhood scale? We found diverse plant communities and distinct spatial patterns across Chicago. Richness and composition of both spontaneous and cultivated plants differed between neighborhoods, with some differences explained by socioeconomic factors such as education. Spontaneous and cultivated plants showed significant spatial autocorrelation, although that spatial autocorrelation generally did not influence neighborhood-scale diversity. Knowledge of these spatial patterns and their socioeconomic drivers could be exploited to increase adoption of environmentally-friendly yard management practices across a city. [ABSTRACT FROM AUTHOR]

This paper evaluates whether or not there is a sprawling tendency to the spatial patterns of warehouse establishments in the Chicago and Phoenix metropolitan areas. The trend of warehouses to move away from the urban centers to more suburban and exurban areas is referred to as "Logistics Sprawl". To measure sprawl, the barycenter of warehousing establishments was compared to the barycenter of all other industry establishments in the region between the years of 1998 and 2013 for Chicago; 1998 and 2015 for Phoenix. This shows that logistics sprawl is a behavior experienced by warehouses in the Chicago area, but not in the Phoenix area. This paper discusses if logistics sprawl is a national trend or a regional behavior by comparing these results to the previous case studies of the Atlanta, Los Angeles, and Seattle metropolitan areas. [ABSTRACT FROM AUTHOR]

Significant circular hotspot detection (SCHD) aims at identifying those circular regions in a spatial space where the occurrence of a particular activity is uncommonly higher than the surrounding areas. Plentiful societal applications make SCHD a problem of utmost interest. In many domains, detection of the most significant circular hotspot (MSCHD) is of further usefulness. Well-timed detection of the most significant hotspot helps crucially for short term response planning (STRP) in situations like a disease outbreak, police vigilance, etc. State of the art methods like SaTScan, identify circular hotspots by listing all possible circles in the search area, followed by a statistical significance test, making it a very high computational cost problem. Considering their high costs, these methods are inefficient in applications related to STRP. To reduce the computational time of SaTScan, two randomized versions of the SaTScan algorithm are presented in this paper. Further, the MSCHD problem is modeled as an optimization problem and three improved variants of Particle Swarm Optimizer (PSO) namely I-PSO, HCL-PSO and Ensemble PSO are applied to detect the most significant hotspots. The comparative and sensitivity analysis are performed using synthetic datasets. The comparative analysis of SaTScan and the presented PSO based schemes is made in terms of the quality of identified hotspots and the computational time. Results reveal that the PSO based schemes (I-PSO, HCL-PSO, and Ensemble PSO) are promising and far efficient than randomized and traditional SaTScan algorithms. Further, for the datasets containing only a single hotspot, the performance of all PSO based schemes is at par with each other. However, for datasets with more than one hotspot in the study area, HCL-PSO has lower average rank than I-PSO and Ensemble PSO schemes and hence seems more promising for MSCHD. The superiority of HCL-PSO based hotspot detection is also validated using Friedman Test. Finally, the presented schemes are applied to the case study of Chicago city for identification of different types of crime hotspots. [ABSTRACT FROM AUTHOR]

This paper provides a case study to show how researchers can apply an innovative counterfactual approach for food safety policy evaluations, particularly in small samples. As an extension to prior studies, I utilize comprehensive variable selection algorithms and generate the optimal combination of counterfactuals. To illustrate the method, I measured public health impacts of a historical food safety intervention. Significant health benefit was found associated with implementation of large-scale milk safety regulation in Chicago. [ABSTRACT FROM AUTHOR]

Characterizing human mobility patterns is essential for understanding human behaviors and the interactions with socioeconomic and natural environment, and plays a critical role in public health, urban planning, transportation engineering and related fields. With the widespread of location-aware mobile devices and continuing advancement of Web 2.0 technologies, location-based social media (LBSM) have been gaining widespread popularity in the past few years. With an access to locations of hundreds of million users, profiles and the contents of the social media posts, the LBSM data provided a novel modality of data source for human mobility study. By exploiting the explicit location footprints and mining the latent demographic information implied in the LBSM data, the purpose of this paper is to investigate the spatiotemporal characteristics of human mobility with a particular focus on the impact of demography. To serve this purpose, we first collect geo-tagged Twitter feeds posted in the conterminous United States area, and organize the collection of feeds using the concept of space-time trajectory corresponding to each Twitter user. Commonly human mobility measures, including detected home and activity centers, are derived for each user trajectory. We then select a subset of Twitter users that have detected home locations in the city of Chicago as a case study, and apply name analysis to the names provided in user profiles to learn the implicit demographic information of Twitter users, including race/ethnicity, gender and age. Finally we explore the spatiotemporal distribution and mobility characteristics of Chicago Twitter users, and investigate the demographic impact by comparing the differences across three demographic dimensions (race/ethnicity, gender and age). We found that, although the human mobility measures of different demographic groups generally follow the generic laws (e.g., power law distribution), the demographic information, particular the race/ethnicity group, significantly affects the urban human mobility patterns. [ABSTRACT FROM AUTHOR]

Seaports have traditionally been the focus of maritime logistics supply chains. Changing production patterns demanding greater end to end visibility by customers and accessibility to key inland population centers assume greater importance in the organization and design of transport resources and cargo flows. While synchronization of all aspects of the supply chain has become an operational necessity for firms, it is often held hostage to the efficiency of hinterland networks who must respond to a large group of stakeholders with sporadic coordination. This is particularly true when looking at the central US city and region of Chicago, a critical intermodal exchange point for truck, air and river barge traffic domestic and global, as well as a major central distribution location. This paper analyzes supply chain integration (SCI) efforts in the metropolitan Chicago region and considers efforts by public and private actors to collaborate for region-wide SCI improvements. Pareto analysis suggests that concentrated freight corridors exist, influencing freight planning for regional transportation networks more directly than diffused regional freight movements. If the corridor service becomes less responsive or congested the corridor will move to different end nodes within the broad region. Regional planning must thus address national, regional, and local moves. Private/public sector infrastructure firms should address functional cooperation on SCI by focusing on corridors as well as local improvements. [ABSTRACT FROM AUTHOR]