Core Districts Research Articles

Creating spatially complete zoning maps using machine learning

Zoning regulates land use and intensity of urban development at the county and municipal level in the United States, promoting economic growth, community health, and environmental preservation. However, limited availability of zoning data at scale hinders regional assessments of regulations and coordinated resilience planning efforts. In this study, we developed an open-source, replicable, and transferable framework to predict spatially complete zoning in areas where zoning information is publicly unavailable. We applied a Hierarchical Random Forest algorithm to predict multilevel zoning districts, including three core districts (residential, non-residential, mixed use) and 13 sub-districts. To mimic real-world data accessibility challenges, we evaluated two models: one filling gaps within a county (within-county) and the other extrapolating for counties with no available data (between-county). We tested our models statewide in North Carolina (NC), USA, and developed the State's first comprehensive zoning map. We found strong predictive performance for our within-county model (∼99% accuracy; macro averaged F1 score of ∼0.97) irrespective of district breakdown (i.e., core and sub). However, our between-county model performance was lower and varied depending on the training counties sampled and the district breakdown considered (19–90% accuracy; macro averaged F1 score of 0.105–0.451). Our framework provides spatially complete zoning maps for previously inaccessible locations, enabling researchers and planners to conduct large-scale comprehensive zoning assessments.

Are Changes in Reported Social-Emotional Skills Just Noise? The Predictive Power of Longitudinal Differences in Self-Reports

Prior work has shown that students’ self-reported levels on social-emotional measures predict achievement levels and gains, but we have little evidence on whether within-student changes in self-reports on social-emotional surveys are predictive of changes in theoretically related academic and behavioral outcomes. We use large-scale data from the California CORE districts to examine whether changes in individual reports on social-emotional measures from one school year to the next predict changes in state math and English language arts test scores and attendance. We show that changes in self-reported social-emotional measures predict changes in both achievement and attendance. These results are robust across model specifications. Moreover, the relationships between SEL and achievement and attendance outcomes are consistent across student subgroups.

Analysis of urban water dissipation characteristics considering anthropogenic impacts: a case study in Beijing

Abstract Urban water dissipation is a significant part of the urban hydrologic cycle and has a typical natural–social dualistic attribute. Besides natural evaporation, the water dissipation in people's daily life and production process cannot be ignored. This study developed an urban water dissipation model based on different land uses and applied it in urban-built areas in Beijing. The results showed that the water dissipation of buildings and green spaces occupied the dominant position, and the water dissipation intensity of each district exceeded 500 mm, among which the six core districts were 700–1,100 mm. Comparing the water dissipation contribution rate and area rate of each underlying surface, it showed that the water dissipation intensity from strong to weak was building, water surface, green spaces, and hardened ground. According to the dualistic analysis of urban water dissipation, the contribution rates of social water dissipation in the six core districts were 45.3–69.1%, which was higher than the 17.8–36.1% of other suburbs obviously. This study reflected that the higher the degree of regional urbanization, the greater the water dissipation intensity, and artificial water dissipation was the main influencing factor.

Singular spectrum analysis (SSA) based hybrid models for emergency ambulance demand (EAD) time series forecasting

Abstract Accepted by: Konstantinos Nikolopoulos One of the challenges of emergency ambulance demand (EAD) time series prediction lies in their non-stationary nature. We study this important problem and propose two hybrid forecasting models, which combine the singular spectrum analysis (SSA) time-series technique with autoregressive integrated moving average (ARIMA) parameterized multivariate forecasting. Both daily and hourly time series are studied. The non-stationary time series are decomposed into three eigentriples by SSA: trends, periodic components and residuals. Selection of the group boundary point of the periodic component is a key issue in the SSA method. We use spectrum analysis to compute a threshold for maximum information content of periodic components. ARIMA mean value prediction models are employed to forecast the trends, periodic components and residuals sub-series. Our research compares ARIMA and SSA-based hybrid models by considering the emergency dispatching departure records of six core districts in Guangzhou city from 1 January 2021 to 31 December 2021. Results show that the integrated SSA-ARIMA model performs best. SSA is a very effective pre-processing method for non-stationary time series prediction. The predictive accuracy of using a hybrid model for hourly EAD time series is higher than that for daily ones. Our discussion should be useful for improving EAD prediction in contexts others than that considered in our research.

The distributive politics of privately financed infrastructure agreements

AbstractPrivately financed infrastructure agreements (PFIAs) are increasingly being used across the globe, bringing private money into the delivery of public goods. How does introducing private actors to such a process change how we think about distributive politics? I investigate this question using both quantitative and qualitative analyses, uncovering a relationship consistent with PFIAs being used as distributive goods and exploring how the credit‐claim potential of PFIAs may affect their distributive use. My quantitative analyses (on 16 middle‐income countries) present evidence suggestive of a relationship between electoral variables and the likelihood of a PFIA being present in a district. In districts aligned with the national ruling party, PFIAs are more likely to be concentrated in swing districts than core districts. I find that this relationship is more pronounced for PFIAs that are more directly attributable to the government. My qualitative press analysis provides insights into how politicians use various features of PFIAs to create credit‐claiming opportunities.

Geospatial-Based Analysis of Balance in Elementary Education—Taking Elementary Schools in Changsha Five Core Districts as Examples

Geospatial-Based Analysis of Balance in Elementary Education—Taking Elementary Schools in Changsha Five Core Districts as Examples

Comparing satellite image and GIS data classified local climate zones to assess urban heat island: A case study of Guangzhou

Cities are frontlines to tackle climate change challenges including the urban heat island (UHI) effect. The classification and mapping of local climate zones (LCZs) can effectively and consistently describe the urban surface structure across urban regions. This study pays attention to two mainstream methods in classifying LCZs, namely, by using geographic information system (GIS) data such as building footprints or remote sensing (RS) satellite images. Little has been done to compare the divergence and coherence of the abovementioned two methods in modeling UHI. Thus, by comparing pairwise LCZ classes of different urban form characteristics in Guangzhou, this study investigated how GIS- and RS-based approaches complement or conflict with each other in explaining the variance of UHI measured by land surface temperature (LST). First, while both GIS-based (R2 0.724) and RS-based (R2 0.729) approaches can effectively explain heat risks measured by LST, the RS-based method slightly outperforms the GIS counterpart. Second, the sizes of LCZs classified by two methods in urban core districts tend to converge but diverge in urban outskirts with disparities in low-rise urban forms. Both approaches found that LCZs with higher heights are all cooler among compact forms. LCZ E is always related to the highest average LST, and LCZ 7, 8, and 10 contribute significantly to heat islands from both GIS and RS results. This study has developed a comparable framework that is evident based for city planners, architects, and urban policy makers to evaluate which approaches can more accurately reveal relations between UHI and urban geometry with land cover.

Water supply in Covid-19 times: the role of public operators, housing associations and informal providers in Arequipa, Peru

ABSTRACT We explore responses of water supply providers during the first stage of the Covid-19 pandemic in Arequipa, Peru, contrasting actions by the public water company, dominant in the city core, and by neighbourhood associations, dominant in the unplanned periphery. The water company implemented instalment payments, the suspension of water shutoffs and the distribution of free water giving priority to the core districts. On the periphery, neighbourhood associations continued to depend mostly on water trucks under irregular and expensive service. The pandemic made more evident the fragmented nature of water supply in cities of the Global South such as Arequipa.

The Considerable Water Evaporation Induced by Human Perspiration and Respiration in Megacities: Quantifying Method and Case Study in Beijing.

The water cycle in urban areas is called the natural-social dualistic water cycle, and it is driven not only by natural forces, but also by human activities. As the drivers of the social water cycle, human perspire continuously, and this is often overlooked as a contributing factor to the water cycle. This paper proposes a method for quantifying the water evaporation induced by human perspiration and respiration in megacities. A calculation based on the sweating prediction model was applied to the city of Beijing to evaluate the evaporation from the human body. The results show that the greatest volume of evaporation produced by human occurs in summer, and the least in spring. The total evaporation produced by human was converted to the evaporation on unit area of the city and reached 5075.2 m3/km2 in the six core districts of Beijing. According to the calculation, the total volume was considerable and reached 14.0 million m3 in 2020, which was equivalent to the annual evapotranspiration from an area of 104.9 km2 of Acer truncatum forest (15 cm diameter at breast height, afforestation density 800 plants/hm2), and even twice the annual total water use in Tartu, Estonia. The results of the study provide a reference for dualistic water cycle research and water cycle flux calculation in urban areas.

Emissions of biogenic volatile organic compounds from urban green spaces in the six core districts of Beijing based on a new satellite dataset

Urban green spaces (UGSs) are often positively associated with the health of urban residents. However, UGSs may also have adverse health effects by releasing biogenic volatile organic compounds (BVOCs) and increasing the ambient concentrations of ozone (O3) and secondary organic aerosols in urban areas. BVOC emissions from UGSs might be underestimated because of the lack of consideration of the UGS land-use type in urban areas. As such, in this study, we used a newly released satellite dataset, Sentinel-2, with a resolution of 10 m, to derive the classification distribution of UGSs and predict the UGS emissions of BVOCs in Beijing in 2019. The results showed that the annual emissions of BVOCs from UGSs were approximately 2.9 Gg C (95% confidence interval (CI): 2.4–3.3) in the six core districts, accounting for approximately 39% of the total UGS emissions in Beijing. Compared with the results based on Sentinel-2, the BVOC emissions might be underestimated by approximately 37% (95% CI: 11–63) using the commonly used satellite dataset. UGSs produced the highest BVOC emissions in summer (from June to August), accounting for 75.2% of the annual emissions. UGSs contributed the most to the O3 formation potential in summer, accounting for 41.5% of the total. We could attribute a considerable amount of the O3 concentration (27.0 μg m−3, 95% CI: 21.4–32.6) to the UGS BVOCs produced in the core districts of Beijing in July. The new BVOC emissions dataset based on Sentinel-2 vegetation information facilitates modeling studies on the formation of surface O3 in urban areas and assessments of the impact of UGSs on public health.

Exploration of urbanization characteristics and their effect on the urban thermal environment in Chengdu, China

As rapid urbanization has drastically and widely altered the Earth's surface, urbanization has become an important contributor to changes in land surface temperature (LST) and the urban thermal environment, which has increasingly become a focus of urban ecologists and environmentalists. The present study used one of the fastest-growing cities in Southwest China, Chengdu city, as an example to explore the urbanization pattern and its effects on surface urban heat islands (SUHIs). To analyze the temporal and spatial characteristics of urban expansion, land use or land cover (LULC) images were classified using a support vector machine (SVM) classifier from Landsat images in 2004–2018, with an overall accuracy of 0.94 and kappa coefficient of 0.90. According to the spatiotemporal LULC changes, Chengdu city expanded at a rapid pace from 80.43 km2/a to 210.50 km2/a in 2004–2018, with an increasing reliance on vegetation area conversion. Spatially imbalanced expansion also occurred. The gravity center moved toward the southeast overall, and peripheral circle districts were the main force of Chengdu's urban expansion compared to the slow-expanding core districts. An allometric model was used to analyze the urbanization pattern, and the results indicated that expansion in Chengdu city was far in excess of its corresponding population growth. The SUHI effect in Chengdu mostly deteriorated over time, and SUHI deterioration closely correlated with the urban expansion area rate and expansion intensity. Therefore, excess urbanization at the expense of vegetation decrease damages the urban ecological environment and accelerates SUHI effects, which threaten habitat health and living comfort.

Understanding the influencing factors of bicycle-sharing demand based on residents’ trips

Bicycle-sharing is an eco-friendly transportation operating model in the context of “Internet Plus” and the sharing economy. It not only meets the short-distance travel needs of residents but has great significance for promoting the sustainable development of urban public transportation. However, a series of problems have appeared in the bicycle-sharing market, such as unreasonable resource allocation, low operating efficiency and management difficulties. Based on booking data and GPS trajectory data in Beijing of Mobike, this paper statistically analyzes the spatial and temporal distribution characteristics of residents’ bicycle-sharing trips. Then, we construct a multi-factor influence model of bicycle-sharing demand based on a negative binomial regression and variable selection model, which quantifies a series of factors that influence bicycle-sharing trips, such as population and the regional economy, building land attributes, transportation accessibility, weather, and climatic conditions, etc. The results show that, firstly, there is a spatial imbalance in the distribution of bicycle-sharing demand among different districts in Beijing. Bicycle-sharing demand is mainly concentrated in the six core districts of the city, with more than 80% of all demand. We also find that the bicycle-sharing demand has different distribution characteristics on working days and nonworking days. Compared with nonworking days, residents’ demand for bicycle-sharing on weekdays shows obvious peak periods in the morning, noon, and evening. Secondly, factors that have a major impact on the demand for bicycle-sharing include: per capita disposable income, pass facilities, parking lots etc. Among them, factors such as per capita disposable income, pass facilities, parking lots and bus/subway stations have a significant positive influence on bicycle-sharing demand. However, the number of functional zones such as airports, ports and marinas, tourist attraction and automobile sales has a significant negative influence. In addition, a comfortable temperature and good air quality encourage residents to use bicycle-sharing more for travel, while high humidity is not conducive to bicycle-sharing. We suggest that companies and related departments should jointly participate in the regulation and management of the bicycle-sharing industry, in various aspects such as bicycle scheduling, bicycle management and industry systems. In this way, cities can allocate bicycle-sharing resources reasonably and improve overall operating efficiency. The advantages of bicycle-sharing can be better used to promote the sustainable development of urban public transportation in the future.

Monitoring and Modeling the Patterns and Trends of Urban Growth Using Urban Sprawl Matrix and CA-Markov Model: A Case Study of Karachi, Pakistan

Understanding the spatial growth of cities is crucial for proactive planning and sustainable urbanization. The largest and most densely inhabited megapolis of Pakistan, Karachi, has experienced massive spatial growth not only in the core areas of the city, but also in the city’s suburbs and outskirts over the past decades. In this study, the land use/land cover (LULC) in Karachi was classified using Landsat data and the random forest algorithm from the Google Earth Engine cloud platform for the years 1990, 2000, 2010, and 2020. Land use/land cover classification maps as well as an urban sprawl matrix technique were used to analyze the geographical patterns and trends of urban sprawl. Six urban classes, namely, the primary urban core, secondary urban core, sub-urban fringe, scatter settlement, urban open space, and non-urban area, were determined for the exploration of urban landscape changes. Future scenarios of LULC for 2030 were predicted using a CA–Markov model. The study found that the built-up area had expanded in a considerably unpredictable manner, primarily at the expense of agricultural land. The increase in mangroves and grassland and shrub land proved the effectiveness of afforestation programs in improving vegetation coverage in the study area. The investigation of urban landscape alteration revealed that the primary urban core expanded from the core districts, namely, the Central, South, and East districts, and a new urban secondary core emerged in Malir in 2020. The CA–Markov model showed that the total urban built-up area could potentially increase from 584.78 km2 in 2020 to 652.59 km2 in 2030. The integrated method combining remote sensing, GIS, and an urban sprawl matrix has proven invaluable for the investigation of urban sprawl in a rapidly growing city.

Environmental Management in cities: A Perspective from the Akure Solid Waste Management Authority, Ondo State, Nigeria.

Management of wastes which is a part of the daily activities of man is an important aspect of environmental management. Over the years, there have been concerted efforts made towards adequately solving the problems created by improper waste management and disposal in Nigeria cities. This study, appraises environmental management in cities; a perspective from the Ondo state Waste Management Authority (OSWMA) Akure. The broad objectives of this study are to identify types of wastes managed by OSWMA, describe the attitude and role of residents (household) toward solid waste management, and examine the possible constraints faced by OSWMA, Akure in the discharge of their duties. Samples were drawn from the total number of OSWMA staff and clients served by the authority in the core, transition and peripheral districts of Akure. 53 questionnaires were administered to the staff representing 10% of the total number of staff while 324 questionnaires representing 1% of residents were administered, collated and analyzed using SPSS version 19. The results of the analysis, revealed that majority (81.1%) of the staff respondents opined that the type of waste managed by the Authority is solid waste; using the door to door method of solid waste collection as submitted by 54.5% of the staff. On the other hand, majority of the households’ respondents (81.4%) expressed willingness to pay for service rendered by the authority. Findings also revealed that poor funding was the main constraints faced by the authority. The study therefore recommends proper funding, provision of equipment and facilities needed for smooth running of the authority in order to ensure effective collection and management of waste in the study area.

Immigration Arrests and Educational Impacts: Linking ICE Arrests to Declines in Achievement, Attendance, and School Climate and Safety in California

With increased tensions and political rhetoric surrounding immigration enforcement in the United States, schools are facing greater challenges in ensuring support for their students of immigrant and Latinx origin. This study examined the associations between county-level immigration arrests and academic achievement, absenteeism, and measures of school climate and safety for students in the California CORE districts. Using ordinary least squares regression analyses with year, grade, school, and student fixed effects, we found that immigration arrests corresponded to declines in academic achievement, attendance, and various measures of school climate and safety for Latinx students and Latinx students who were English learners. We also find small declines in measures of school climate and safety for students who ever received special education services via an Individualized Education Program. Associations were strongest for arrests that occurred during the Trump administration compared with those that occurred during the second term of the Obama administration. Policy implications are discussed.

A study on the relationship between "Plan on construction of government facilities and other public office facilities (Icidanchi Plan)" , "Plan on integration of the Civic Core districts" and the castle district.

近世城下町を基盤とする主要な地方都市のうち、城郭地区及びその周辺で一団地の官公庁施設計画やシビックコア地区整備計画が策定された７都市を対象として、官庁街再編計画と城郭地区との関係性を明らかにすることを目的とする。明らかになったのは、第１に、一団地計画では城郭地区の中心性を利用していたのに対してシビックコア計画では城郭地区の歴史性を利用していたこと、第２に、一団地計画では城郭地区の空間要素に配慮した施設計画がみられ、シビックコア計画では城郭地区の再生と連動した施設計画がみられたことである。

An IRT Mixture Model for Rating Scale Confusion Associated with Negatively Worded Items in Measures of Social-Emotional Learning

ABSTRACT We illustrate the application of mixture IRT models to evaluate respondent confusion due to the negative wording of certain items on a social-emotional learning (SEL) assessment. Using actual student self-report ratings on four social-emotional learning scales collected from students in grades 3–12 from CORE Districts in the state of California, we also evaluate the consequences of the potential confusion in biasing student- and school-level scores as well as the estimated correlational relationships between SEL constructs and student-level variables. Models of both full and partial confusion are examined. Our results suggest that (1) rating scale confusion due to negatively worded items does appear to be present; (2) the confusion is most prevalent at lower grade levels (third–fifth); and (3) the occurrence of confusion is positively related to both reading proficiency and ELL status, as anticipated, and consequently biases estimates of SEL correlations with these student-level variables. For these reasons, we suggest future iterations of the SEL measures use only positively oriented items.

The electoral benefits of unemployment, clientelism and distributive politics

Abstract We show that in the month prior to the 2003 Argentine presidential election, the expenditures of an Argentine poverty relief program exhibit a partisan bias. Taking into consideration the number of potential recipients (the unemployed with children 18 years old or less), the counties that were ideologically against the incumbent received a disproportionately large amount of the spending. Hence, instead of targeting swing or core districts, as established in the distributive politics literature, in Argentina the incumbent targeted opposition strongholds. To identify these effects and to classify districts into swing, core and opposition ones, we exploit the 2001 crises in Argentina. In particular, we use the 2001 political discontent that led to 15% of blank and null votes to estimate swing districts. Finally, we also show that this tactical spending had an electoral purpose. In counties where it was arguably cheaper to monitor the recipients’ voting behavior, the spending was significantly larger. In return, the clientelist exchange took place in districts with greater spending and monitoring. Thus, the politicians’ incentives to design an appropriate poverty relief program may be at odds with their electoral motivation.

Policy Narratives of Accountability and Social-Emotional Learning

The paradigm of test-based accountability has been a dominant force for decades, yet some argue that we have recently witnessed a dramatic change in the key beliefs influencing educational policy. To understand the extent of this transformation, we investigated the policy narrative supporting the adoption of a multiple measure accountability system in California’s CORE Districts. Our data revealed a narrative integrating key beliefs about knowledge, accountability, and social-emotional learning. This narrative continues the major themes of the previous accountability paradigm, but with two notable differences regarding the use of measurement data and role of social-emotional learning.

Building consensus: shifting strategies in the territorial targeting of Turkey's public transport investment

ABSTRACT A growing amount of research explores how the allocation of regional development monies follows electoral reasons. Yet, the existing literature on distributive politics provides different and contrasting expectations on which geographical areas will be targeted. The paper focuses on proportional representation (PR) systems. While in such settings governments have incentives to target core districts and punish foes, it is suggested that when incumbents attempt to build a state–party image they may broaden the territorial allocation of benefits and even target opposition out-groups. The paper exploits data on Turkey's public transport investment for the period 2003–14 and in-depth interviews to provide results in support of the hypothesis.