As China is transitioning from a planned economy to a market-based economy, over the past 30 years, China’s economy has experienced the same type of changes that led to amenity-driven housing and land markets in more developed countries. This paper examines the effect of different types of amenities on urban land and housing prices across Chinese cities in 2020. When firms and households value some amenities, the assumption is that the preference for these amenities will be valued and reflected in both land and housing markets. We conduct a cross-sectional analysis of urban land and housing prices in prefecture and higher-level cities in China. We extend the Roback model to explore the extent to which the different land rents and housing prices reflect the compensations for interregional amenity differences across Chinese cities. We include two types of amenities: natural amenities and locally produced amenities. We perform two-stage least squares (2SLS) estimations and compute the implicit prices of various amenities for households and firms. The empirical results show that a range of amenities is valued by both households and firms, resulting in higher housing and industrial prices.

This article aims to examine the characteristics of cities where New Urbanism (NU) developments are located as of 2019. We first develop a set of hypotheses to explore why some cities are welcoming NU developments more than other cities and how the cities differ in terms of general real estate development determinants, fiscal capacity and regulatory authority, advocacy group support, and cultural diversity. We then employ a Negative Binomial Regression to test the relationship between concentrations of NU developments and a variety of city characteristics by using a data set of 6923 urban cities. The results suggest that NU developments are advocated by cities with a higher level of environmental awareness, better fiscal and regulatory status, and better cultural diversity. The research results highlight the importance of continuously gaining support from environmental groups and the general public for effective expansion of New Urbanist developments within the U.S. These findings also indicate that for noteworthy changes in growth patterns to arise at a large scale across the U.S., there must be changes in values and preferences, and institutional capacity in updating land-use regulations that allow for sustainable growth.

Probe-vehicle sensing can extend traffic observation beyond fixed detectors, but its sparse and uneven coverage complicates the interpretation of predicted traffic states. This study develops a mask- and coverage-aware framework for regional traffic-state assessment from sparse probe-vehicle speed observations. Segment-level data from Xuzhou, China, were aggregated into 32 regions at 15 min resolution; only 0.157% of segment–time cells were observed. A regional target was observable whenever at least one segment in its region was observed, and the observed-target ratio across the controlled test conditions ranged from 12.1% to 23.9%. Nine controlled-missingness conditions were used to evaluate one-step speed prediction, followed by deterministic evidence-fusion assessment using predicted speed, observation masks, coverage history, and neighboring-region evidence. Reference labels for 130 stratified region–time cases were independently assigned and adjudicated by two annotators. Among the evaluated deterministic variants on this cohort, the speed-plus-mask rule achieved the strongest overall classification performance, with 69.2% accuracy and 63.7% Macro-F1. Because 69.2% equals the majority-class share of the cohort by construction (90 of 130 cases are low severity), the rule’s advantage is its cross-class discrimination (Macro-F1) rather than overall accuracy alone. Adding coverage reduced the false-severe rate from 5.6% to 2.8%, but also reduced overall classification performance, demonstrating an over-escalation-control–classification trade-off. On a limited 16-case protocol, a fixed, schema-constrained large language model achieved 97.5% modal-label repeatability across five repetitions but only 32.0% Macro-F1; its limited and imbalanced support precludes any full-cohort effectiveness claim. The findings show that sensing support should be retained as traffic-state assessment evidence and reported alongside prediction and traffic-state alert performance. On the same 130 cases, a balanced RBF-SVM reached 75.0% mean grouped out-of-fold accuracy and 64.6% Macro-F1, but all four headline-metric paired intervals versus speed plus mask crossed zero and its false-severe rate was higher (9.7% versus 5.6%); no method was uniformly superior.

Ride-hailing global positioning system (GPS) mobile probe data provide flexible urban traffic observations, but their sparse and uneven coverage makes model evaluation difficult because observed targets, valid predictions, and historical input support do not always coincide. This study audits ultra-short-term road-segment speed forecasting under sparse mobile sensing using a mask-consistent support-chain framework. A three-day GPS dataset is aggregated into 5 min speed observations over 1970 road segments and used as a controlled sparse-sensing case study rather than a general-purpose long-term forecasting benchmark. The evaluation protocol distinguishes the full test grid, the set of directly observed target speeds, model-valid prediction support, strict complete-history support, and common-support subsets for coverage-limited baselines. The directly observed target set is used as the primary relaxed support because it retains all verifiable ground-truth targets, while strict and common-support subsets are reported as sensitivity checks. Under this support-conditioned evaluation, the adaptive graph convolutional recurrent network (AGCRN) is associated with lower mean absolute error (MAE) among full-coverage models, the historical mean (HIST_MEAN) baseline is associated with lower root mean squared error (RMSE), and congestion recall remains below 0.24 for all full-coverage deep models. These complementary results indicate conditional and metric-dependent strengths rather than universal model superiority. Because the dataset covers only three consecutive days, weekday/weekend variation, incident-specific fluctuations, seasonal effects, and spatial transferability cannot be fully examined and are treated as limitations. Overall, the findings show that evaluation support should be reported as a first-order experimental factor alongside model accuracy under sparse mobile probe sensing.