City Of Dallas Research Articles

The mechanism of using the geographic information system in detecting unsafe spaces in the municipal areas of big cities (Investigation of unsafe places in the city of Dallas, Texas, USA)

For a long time, identifying the environmental and spatial factors of crime and trying to eliminate these factors or reduce their effects has been known as the most important way to increase safety and prevent the occurrence of anomalies. Because social anomalies, no matter how they happen to humans, have a place and place and time characteristics. Which distinguishes these behaviors from each other. Surveys show that some urban spaces have a higher crime rate due to their physical, social, and economic characteristics; a city is a collection of special mutual narratives that originate from the role and context of its internal institutions. Therefore, without providing a subjective definition, we accept the existence of the city based on objective characteristics, that is, the quality of the structure of the society and its economic and administrative role, not just the population quantity. Also, some thinkers have provided a comprehensible definition of urban space "Urban space is an organized, decorated, and orderly structure for human activities". Part of human actions and activities take place in place and space, and according to Anthony Giddens, "space is a physical set for social interactions". And on the contrary, some spaces prevent and prevent crimes from being committed inside them. On the other hand, research has proven that criminals often make rational choices when committing crimes and look for the simplest, least dangerous, and most suitable opportunities and conditions in place and time to commit crimes.

Time of week intensity estimation from partly interval censored data with applications to police patrol planning

Law enforcement agencies are tasked with crime prevention and crime reduction under limited resources. Having an accurate temporal estimate of the crime rate would be valuable to achieve such a goal. However, estimation is usually complicated by the interval censored nature of crime data. We cast the problem of intensity estimation as a Poisson regression using an EM algorithm to estimate the parameters. Two special penalties are added that provide smoothness over the time of day and day of week. This approach provides accurate intensity estimates and can also uncover day of week clusters that share the same intensity patterns. Both simulated and real crime data gathered from the city of Cincinnati and the city of Dallas are used to demonstrate the effectiveness of the proposed model.

Inhabiting digital spaces: An informational right to the city for mobility justice

Space is often produced digitally before it is produced physically. This article investigates how the right to the city can be broadened to include the appropriation of digital spaces to produce ‘lived’ transportation spaces. Focussing on mobilisation against highway expansion in Dallas, Texas, we ask the following: (1) what are the mechanisms through which space is conceived, perceived, and lived through the lens of mobility justice; (2) how might claims for technical information challenge dominant transportation policies and projects; and (3) how might participants inhabit digital spaces? We conduct a qualitative analysis of transportation planning narratives, visualisations, and public comments in three documents: the Dallas City Center Master Assessment Process, Coalition for a New Dallas’ I-345/45 Framework Plan, and public survey data regarding proposed highway changes ( n = 1241). Findings demonstrate how residents challenge transportation ‘needs’ as often determined in conceptual planning spaces. Further, technologies can be appropriated to produce differential spaces, which can alter the trajectory of highway projects. Challenging the legitimacy of institutionalised knowledge through the appropriation and production of digital spaces forms part of a larger claim to the right of the city.

Geography, Infrastructure and Architecture: From the Immaterial Scenes of the Arts to the Physical Space of the American City

The aim of this paper is to propose a design answer to the United States issue of public space through the use of art, using the city of Dallas as an example. A pragmatic way to interpret space is the grid. The first analysis are aimed toward one of the most ancient systems of formulating “urban” conglomerates, the roman grid. In the USA, it is the Continental Congress’s Land Ordinance of 1785 to prescribe the usage of the Continental grid. Ideally the two grids have the same role, the significant difference is their scale. This grid can only be compared to a colossal scale, here space is subordinated to time. This mutation is in line with the urban development processes of the city of Dallas. This is why, the University Crossing Trail Public Improvement Distric, along with the Southern Methodist University of Dallas, have developed a collaboration to promote and regenerate an old trail of the city into an art corridor. Even if not constructed using the Jeffersonian grid, the apparent orderliness of its blocks accentuates the complete supremacy of circulation, while the shapes and turns of the infrastructure collaborate to a new idea of beauty within the landscape of the city. The proposed solution is to intervene with three different urban art projects that have been placed along the main and different types of infrastructures of the city. The intention is to invert the subordination of space that returns protagonist where the urban art projects have been inserted, without ever negating the principal condition of circulation and speed. Because this apparent contradiction requires a specific and cautious sensitivity, it is the responsibility of art and architecture to mediate between audacious locations, functional solutions and the world of visual representation.

An E-scooter route assignment framework to improve user safety, comfort and compliance with city rules and regulations

The rapid expansion of dockless shared micromobility systems, particularly dockless e-scooters, presents major opportunities along with challenges that have led some cities to ban these services within their jurisdiction. The safe reintroduction of dockless shared micromobility services requires support through enhancement of e-scooter service implementation, improvement of rider behavior, and enforcement of rules and regulations. This paper develops the micromobility guidance tool (MGT) that guides a shared micromobility user going from a defined origin to a defined destination to a route that meets multiple criteria, including safety, comfort, and compliance. Thus, this tool emphasizes two network features, the type of infrastructure and the condition of infrastructure. Moreover, the tool locates shared micromobility parking that is closest to a user’s destination and sends alerts to avoid prohibited infrastructure types. For each trip, the proposed tool uses a shortest path routing algorithm to assign its route and the Euclidean distance to assign closest parking. A generalized cost function was developed to estimate the time needed to cross a link while considering e-scooter accessibility, user routing preferences, and ordinance restrictions. The effectiveness of MGT was tested through a case study of tens of thousands of dockless shared e-scooter trips within the City of Dallas, TX. As a validation step, several experiments were carried out to examine the developed tool under various scenarios. Routes generated by MGT were compared to actual routes taken by users and to scenarios corresponding to models previously published in the literature. When compared to actual routes taken by riders, case-study findings showed a notable increase in the use of preferred types of infrastructure (bike lanes, sidewalks, one-way roads, etc.), the use of bike infrastructure (bike lanes and bike trails), and the use of better conditions (i.e., less deficient infrastructure). Additionally, a marked reduction in regulatory violations was exhibited. The methodological approach presented here could be generalized to any city network or shared micromobility system. The proposed tool could be required by cities and adopted by shared micromobility providers seeking to improve rider safety, comfort, and compliance. This tool could be integrated into the provider application guiding the user to micromobility-friendly routes and designated parking, a feature not yet available to micromobility users. Finally, this paper has offered policy recommendations and guidance for practice, where the reintroduction of shared e-scooters is being considered by cities and expansion considered in new markets.

A planning framework to mitigate localized urban stormwater inlet flooding using distributed Green Stormwater Infrastructure at an urban scale: Case study of Dallas, Texas

Mitigation of localized pluvial flooding is one of the major resiliency goals in urban environments, and Green Stormwater Infrastructure (GSI) has the potential to deliver such an outcome. However, there is a lack of systematic approaches to prioritize investment in different candidate areas. This study provides a framework to identify vulnerable stormwater drainage inlets and their contributing areas, prioritize them, identify dominant factors in their selection, assess the potential of GSI in mitigating their overflows, and compare the impact and its cost to gray infrastructure upgrade alternatives. Using SWMM 5.1.013, decision trees, and a volumetric-based assessment of GSI overflow capture, we applied the framework to the City of Dallas, Texas, for three design storms with three GSI practices— bioretention cells, raingardens, and rainwater harvesting tanks. Results showed that there was a significant increase in the number of overflowing stormwater drainage inlets, referred to as hotspots, and their contributing subwatersheds, referred to as opportunity areas, with more intense storms especially in problematic watersheds. Also, prioritization results provided a series of maps to rank the opportunity areas based on overflow severity, recurrence of the overflows, and GSI availability. Moreover, classification results showed that inlet features, especially the inlet depth, were the dominant factors in the identification of the non-problematic inlets. Finally, the GSI impact assessment showed substantial overflow mitigation even at the “very high” severity levels when GSI is comprehensively deployed across opportunity areas. Despite gray infrastructure upgrades yielding higher reduction levels, their cost per cubic meter was higher than GSI. Therefore, a combination of GSI and gray results in maximum overflow reduction at a lower cost compared to common practices.

Estimating the likelihood of roadway pluvial flood based on crowdsourced traffic data and depression-based DEM analysis

Abstract. Water ponding and pluvial flash flooding (PFF) on roadways can pose a significant risk to drivers. Furthermore, climate change, growing urbanization, increasing imperviousness, and aging stormwater infrastructure have increased the frequency of these events. Using physics-based models to predict pluvial flooding at the road segment scale requires notable terrain simplifications and detailed information that is often not available at fine scales (e.g., blockage of stormwater inlets). This brings uncertainty into the results, especially in highly urbanized areas where micro-topographic features typically govern the actual flow dynamics. This study evaluates the potential for flood observations collected from Waze – a community-based navigation app – to estimate the likelihood of PFF at the road segment scale. We investigated the correlation of the Waze flood reports with well-known flood observations and maps, including the National Flood Hazard Layer (NFHL), high watermarks, and low water crossings data inventories. In addition, highly localized surface depressions and their catchments are derived from a 1 m resolution bare-earth digital elevation model (BE-DEM) to investigate the spatial association of Waze flood reports. This analysis showed that the highest correlation of Waze flood reports exists with local surface depressions rather than river flooding, indicating that they are potentially useful indicators of PFF. Accordingly, two data-driven models, empirical Bayes (EB) and random forest (RF) regression, were developed to predict the frequency of flooding, a proxy for flood susceptibility, for three classes of historical storm events (light, moderate, and severe) in every road segment with surface depressions. Applying the models to Waze data from 150 storms in the city of Dallas showed that depression catchment drainage area and imperviousness are the most important predictive features. The EB model performed with reasonable precision in estimating the number of PFF events out of 92 light, 41 moderate, and 17 severe storms with 0.84, 0.85, and 1.09 mean absolute errors, respectively. This study shows that Waze data provide useful information for highly localized PFF prediction. The superior performance of EB compared to the RF model shows that the historical observations included in the EB approach are important for more accurate PFF prediction.

CONDITION PREDICTION OF SANITARY SEWERAGE PIPELINE SYSTEMS USING MULTINOMIAL LOGISTIC REGRESSION

It is important for the sanitary sewerage pipeline system to be in a good condition for providing safe conveyance of the wastewater from households, businesses, and industries to the wastewater treatment plants. Water utilities inspect sewer pipelines to decide which segments of the sanitary sewer pipes need renewal or replacement. This process of inspecting the sewer pipes is described as condition assessment. The objective of this paper is to develop Multinomial Logistic Regression (MLR) to predict the condition rating of sanitary sewerage pipelines using inspection and condition assessment data. MLR model was developed from the City of Dallas's data. The MLR model was built using 80% of randomly selected data and validated using the remaining 20% of data. The significant physical factors influencing sanitary pipes condition rating included diameter, age, pipe material, and length. Soil type was the environmental factor that influenced sanitary sewer pipes condition rating. The accuracy of the performance of the MLR was found to be 75%. This developed model will help the policymakers and sanitary sewer utility managers to predict sanitary sewer pipes condition rating that enables to prioritize the sanitary sewer pipes to be rehabilitated and/or replaced.

Emerging from disruptions and ambiguities: Understanding local government innovative responses during the challenges of the COVID-19 pandemic

This article investigates the utility of an integrated approach which combines the Incident Command System (ICS) model and the Disruption-Ambiguity-Innovation-Challenge model, for understanding local government homeless service responses during the COVID-19 pandemic. A case study in the City of Dallas, Texas is used to understand how three functional areas of the ICS (authority, operations, and finances), were influential in creating disruptions, ambiguities, innovations, and challenges for local government and its nonprofit partners. By using an integrated approach, we identify challenges, learning outcomes, and action strategies useful for consideration for local governments’ strategies for crisis management.

Come Hell or No Water: The Story of Sandbranch and the Unincorporated Community Fight for Public Services

Sandbranch is the only unincorporated community left in Dallas County, and the residents of this majority-Black, impoverished community have had their cries for basic necessities—such as clean, running water—largely ignored. With the County and the City of Dallas not remedying the problem so far, there is a question as to who is responsible for providing water and other public services to the community’s eighty residents. As it currently stands, Texas law simply permits local governments to offer assistance to unincorporated communities but does not mandate that affirmative measures be taken to ensure that these communities are provided for. What is the scope of the existing local government laws when it comes to getting public services to unincorporated areas, and what will it take for Sandbranch to finally get the resources it has been fighting to receive for decades?

Development and Comparison of Prediction Models for Sanitary Sewer Pipes Condition Assessment Using Multinomial Logistic Regression and Artificial Neural Network

Sanitary sewer pipes infrastructure system being in good condition is essential for providing safe conveyance of the wastewater from homes, businesses, and industries to the wastewater treatment plants. For sanitary sewer pipes to deliver the wastewater to the treatment plants, they must be in good condition. Most of the water utilities have aged sanitary sewer pipes. Water utilities inspect sewer pipes to decide which segments of the sanitary sewer pipes need rehabilitation or replacement. The process of inspecting the sewer pipes is described as condition assessment. This condition assessment process is costly and necessitates developing a model that predicts the condition rating of sanitary sewer pipes. The objective of this study is to develop Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) models to predict sanitary sewer pipes condition rating using inspection and condition assessment data. MLR and ANN models are developed from the City of Dallas’s data. The MLR model is built using 80% of randomly selected data and validated using the remaining 20% of data. The ANN model is trained, validated, and tested. The significant physical factors influencing sanitary pipes condition rating include diameter, age, pipe material, and length. Soil type is the environmental factor that influences sanitary sewer pipes condition rating. The accuracy of the performance of the MLR and ANN is found to be 75% and 85%, respectively. This study contributes to the body of knowledge by developing models to predict sanitary sewer pipes condition rating that enables policymakers and sanitary sewer utilities managers to prioritize the sanitary sewer pipes to be rehabilitated and/or replaced.

Re-Opening Dallas: A Short-Term Evaluation of COVID-19 Regulations and Crime

We investigated the relationship between COVID-19 stay-at-home regulations and property and violent crime indexes in Dallas, TX during the first 6 months of 2020. We tested for changes in property and violent crime trends using four key “intervention” dates: the stay-at-home order issued by Judge Clay Jenkins (March 24), the start of Governor Abbott’s phase one of re-opening (May 1), a second phase of more widespread re-openings (May 18), and a third phase of increased capacity limits for businesses (June 3). Our analyses point to two main findings: (1) the time between the initial stay-at-home policy and the phase one re-opening was associated with an increase in the trend of both violent and property crime (although at lower levels than pre-pandemic); and (2) the third phase of re-opening the City of Dallas was associated with higher daily counts of property and violent crime. Our findings suggest that policy makers need to consider policies not only related to police enforcement but also allocation of other social services, particularly when such a sudden policy (e.g., stay-at-home order) is implemented.

Racial Disparities in Crime Victimization during the COVID-19 Lockdown.

The study examines racial differences in crime victimization rates among Black, Hispanic and White individuals during the stay-at-home order in Dallas city, TX. The study is based on sample of 85,958 calls for service recorded by Dallas Police Department between February 13, 2019, and April 30, 2020, where victims have been identified and their race is known. The findings suggest that Blacks were less likely to experience unintended drop in crime rates during the lockdown and also less likely to see decline in crimes against property when compared to both White and Hispanic victims. At the same time Blacks were more likely to experience increase in crimes committed at the apartments during the lockdown with Black females experiencing higher increase than Black males. There were no differences in crime victimization for incidents occurred in public places and crimes against persons.

Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas

The urban heat island effect has been studied extensively by many researchers around the world with the process of urbanization coming about as one of the major culprits of the increasing urban land surface temperatures. Over the past 20 years, the city of Dallas, Texas, has consistently been one of the fastest growing cities in the United States and has faced rapid urbanization and great amounts of urban sprawl, leading to an increase in built-up surface area. In this study, we utilize Landsat 8 satellite images, Geographic Information System (GIS) technologies, land use/land cover (LULC) data, and a state-of-the-art methodology combining machine learning algorithms (eXtreme Gradient Boosted models, or XGBoost) and a modern game theoretic-based approach (Shapley Additive exPlanation, or SHAP values) to investigate how different land use/land cover classifications impact the land surface temperature and park cooling effects in the city of Dallas. We conclude that green spaces, residential, and commercial/office spaces have the largest impacts on Land Surface Temperatures (LST) as well as the Park’s Cooling Intensity (PCI). Additionally, we have found that the extent and direction of influence of these categories depends heavily on the surrounding area. By using SHAP values we can describe these interactions in greater detail than previous studies. These results will provide an important reference for future urban and park placement planning to minimize the urban heat island effect, especially in sprawling cities.

Transit Deserts: Equity analysis of public transit accessibility

Areas, where disadvantaged and transit-dependent populations are provided with inadequate amounts of transit supply, can be labeled transit deserts. Exploring transit deserts may help transit agencies improve accessibility to services while improving transit distribution and equity. This study utilizes the concepts of transit demand and transit supply to identify transit deserts in the City of Dallas. The comprehensive public transit accessibility (CPTA) score is introduced to evaluate accessibility to transit and to provide a holistic view of transit supply. While previous studies have primarily used proximity or spatial indicators to describe accessibility, this study utilizes a comprehensive set of spatial and temporal measures (connectivity to the network, connectivity to destinations, service frequency, flexibility, and time efficiency) to estimate accessibility to transit. Overlapping areas of high demand (transit dependency) and low supply (as measured using the CPTA) are then characterized as transit deserts. The CPTA score and its application to the analysis of transit deserts provides a universal framework which can be broadly applied by transit agencies, city officials, and transit stakeholders. The contributions of this study are as follows: first, developing a comprehensive framework to estimate transit supply or accessibility; and second, introducing a methodology for identifying transit deserts.

How Local Governments Are Caring for the Homeless During the COVID-19 Pandemic

This article discusses the plight of the homeless during public health emergencies and the coronavirus disease of 2019 (COVID-19) pandemic. It reviews the role of public administrators that grounds their efforts by examining their foundational purpose to serve the most vulnerable in our society. Using subsidiarity principle as the context, it discusses homelessness in America and the role of the federal Department of Housing and Urban Development and their Continuum of Care program. It also highlights the role of the Centers for Disease Control and Prevention during public health emergencies and their interim guidelines for local governments in providing for the homeless during emergencies. Finally, through a case study on the city of Dallas, Texas, the article examines how local governments have responded to address the needs of the homeless during the COVID-19 pandemic. It concludes that it is imperative that public administrators at all levels of government explore areas of shared competence, cooperation, and allocate responsibility where it would yield the most efficient result.

Framework for network-level pavement condition assessment using remote sensing data mining

Pavement condition monitoring is fundamental for the efficient allocation of resources in transportation asset management. However, data collection involves laborious and costly procedures. Our study intends to investigate the usage of remote sensing data for network-level pavement condition assessment that offers a more cost-effective alternative and a rapid infrastructure assessment tool that can be used in the aftermath of natural disasters. Based on an extensive literature review, a data mining framework was established to train models that predict the pavement condition of different road segments. The framework exploits the inherent information of multispectral images by generating spectral related attributes. To identify pavement sampling areas, an automated procedure using image segmentation replaces manual surface digitizing. Unlike previous research, different classification models were used to approximate the mapping function from spectral information to pavement conditions. A preliminary case study was conducted with data provided by the City of Dallas and multispectral images acquired from the Texas Natural Resources Information System. The mean-shift segmentation algorithm was used to locate noise introducing areas on the pavement surface. Four different classification models were trained using k-nearest neighbors, naive Bayes, support vector machines, and a multilayer perceptron. The developed models were employed to predict the road surface condition class of a test set not included in the training procedure. The multilayer perceptron presented the highest accuracy level of 71%, showing that the framework might have the potential for future implementation.

Crowdsourced Mapping for Healthy Food Accessibility in Dallas, Texas: A Feasibility Study.

Since its first use for describing a neighborhood lacking access to food in the 1990's, “food deserts” has been widely addressed by researchers and adopted as an indicator of neighborhood-level food insecurity by governmental agencies, such as USDA. However, mostly due to cost and difficulty in collecting georeferenced data and characteristics of grocery stores, the USDA Food Access Research Atlas is infrequently released, and considers only income, vehicle ownership, and distance to the nearest grocery store. In this paper, we explored the feasibility of a crowdsourced geospatial data source, coupled with additional measures, in supplementing the USDA's current designation of food deserts. We used Yelp data to map food deserts in the city of Dallas and compared them with those based on the 2015 USDA food retailer database. Although direct comparison was not possible due to time mismatch between the two data sources, the discrepancies highlighted the need of a more frequent identification of food deserts for timely policy intervention. Furthermore, we extended mapping to reveal other potential areas of concerns, by adding the Transit Score metric and Yelp's price descriptor of businesses. The resulting maps illustrated the areas with grocery stores nearby but with limited accessibility due to lack of public transit or potential financial barriers in purchasing foods due to high prices. Our findings demonstrate the current status and future potential of up-to-date crowdsourced, georeferenced data as a complement of official government data, which could serve to extend food access research and guide health policies.

Demand Metric for Supermarket Site Selection: A Case Study

Food deserts are lower-income communities with limited access to mainstream supermarkets and healthier food choices, a problem that often leads to increased levels of diabetes and obesity. In 2018, The Department of Agriculture identified 88 food deserts in Dallas County, 51 percent of which were concentrated in three southern sectors of the City of Dallas. Despite offering a $3 million incentive and tax abatements, the City of Dallas has made little progress in attracting a mainstream supermarket to South Dallas, a community that is often associated with higher crime and poverty rates, and minimal economic potential. Unlike a tradition location-allocation study that identifies suitable locations for new facility placement, this study focuses on the development of a new metric for estimating spatial patterns of demand for food-at-home expenditures. This metric can be used to inform traditional supermarket site selection studies in lower income urban communities.

The Evolution of Uneven Development in Dallas, TX

Dallas has a long history of uneven development. It is the product of excess capital, white planning, and a desire to shape the land into something it is not. Communities in Dallas broke sharply along racial and class lines, and as a result black and white Dallas developed separately. Forces of structural and physical violence largely determined where African American neighborhoods were, and are, located in Dallas. African American, Mexican American, and other low-income communities suffered not only from low housing availability and high rent prices, but also bombings, arson, and other physical threats. When alliances formed between poor whites and their neighbors of color, the construction of a highway or railroad was apt to split a neighborhood and fracture the community. The effects of segregation and discrimination have followed the African American communities in Dallas since their inception. Space for African Americans, who made up almost twenty percent of the population, within the Dallas city limits continued to shrink. In 1940, African American neighborhoods were squeezed into 3.5 square miles within the City and in small communities along the perimeter. Meanwhile, the wealthy sequestered themselves into enclaves within the city, avoiding both minorities and municipal taxes while benefitting from city services. In this paper I explore how this historical discrimination and segregation shaped geographic inequality across Dallas today. Much of the wealth in Dallas is clustered in the north around the Park Cities enclave as illustrated by viewing the property tax values over the city. Low-income, majority African American neighborhoods like Joppa, located in southern Dallas, illustrate the impacts that the flows of capital have on livelihoods. South Dallas experienced a sharp decrease in population as residents moved to the suburbs in the 1960s and has since been underdeveloped. Joppa, a small, historically neglected, neighborhood has remained isolated until recently. Developers are interested in Joppa for its cheap, empty lots, and valuable proximity to booming downtown Dallas and the Trinity River Corridor. Gentrification is a concern for the neighborhood; residents have a desire to revitalize their neighborhood on their own terms, not developers’. This research will help to visualize and amplify the continued material effects of a history Dallas is trying to make invisible.