Browsing by Author "Peng, Qiong"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Housing Value and Light Rail Transit Construction: Evidence from Three Essays(2020) Peng, Qiong; Knaap, Gerrit Jan; Urban and Regional Planning and Design; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In three essays, this dissertation explores what’s the determinants of multifamily rents and whether an anticipated investment in light rail transit influences multifamily rents and single-family housing prices in the rail transit pre-service period. In the first essay, I applied a multilevel linear model approach to account for the multifamily housing hierarchical data structure, and assessed the effects of service provision and management on multifamily rents. The findings show that pet allowance, availability of a short-term lease, and storage service increase rents significantly, while general renovations and availability of services for those with disabilities do not increase rents. The second essay empirically tests whether light rail transit in the pre-service period impacts multifamily housing rent in the transit corridor. Two approaches, a first-difference method and a difference-in-difference method, are used to test the research question. The results indicate that the rents of two-bedroom, three-bedroom, and four-bedroom units within a half-mile from planned light rail stops have significantly increased from 2015 to 2018 compared with the rent of units in other areas in Montgomery County. The third essay examines the temporal and spatial variation of the effect of the Purple Line on single-family home prices during the rail line pre-service period. The results show that the housing market saw a premium in 2012, the year the Purple Line project progressed into the preliminary engineering phase. The results also show that the effect of the new light rail transit line is distributed unevenly across the catchment areas of newly built stations and established stations.Item Silver Spring Civic Building and Veteran’s Plaza Economic Impact Analysis FY-2017(Partnership for Action Learning in Sustainability (PALS), 2018) Eom, Hunyjoo; Peng, Qiong; Wortham, Morgan; Dempwolf, Scott; Knapp, GerritThe Silver Spring Regional Center sought a report detailing the economic and fiscal impacts of the Silver Spring Civic Building at Veteran’s Plaza (SSCB) on Montgomery County’s economy in 2017. The SSCB is an indoor community facility for events, festivals, trade shows, conferences and conventions, and other activities available to county residents and out-of-town groups. The County benefits from the economic activity and fiscal revenue generated by the SSCB’s meetings and events. While some of these benefits are difficult to measure, the SSCB’s contribution to regional economic activity can be quantified in terms of spending, jobs and earnings. This analysis compiles data from a variety of sources to estimate direct, indirect, and induced economic benefits and tax benefits produced by the SSCB in FY2017. This economic and fiscal impact analysis was conducted by the Partnership for Action Learning in Sustainability (PALS) and the Center for Economic Development (EDA Center) at the University of Maryland College Park. To quantify the SSCB’s economic and fiscal impact, PALS/EDA used the IMPLAN input/output model.Item Spatiotemporal Prediction of Theft Risk with Deep Inception-Residual Networks(MDPI, 2021-01-29) Ye, Xinyue; Duan, Lian; Peng, QiongSpatiotemporal prediction of crime is crucial for public safety and smart cities operation. As crime incidents are distributed sparsely across space and time, existing deep-learning methods constrained by coarse spatial scale offer only limited values in prediction of crime density. This paper proposes the use of deep inception-residual networks (DIRNet) to conduct fine-grained, theft-related crime prediction based on non-emergency service request data (311 events). Specifically, it outlines the employment of inception units comprising asymmetrical convolution layers to draw low-level spatiotemporal dependencies hidden in crime events and complaint records in the 311 dataset. Afterward, this paper details how residual units can be applied to capture high-level spatiotemporal features from low-level spatiotemporal dependencies for the final prediction. The effectiveness of the proposed DIRNet is evaluated based on theft-related crime data and 311 data in New York City from 2010 to 2015. The results confirm that the DIRNet obtains an average F1 of 71%, which is better than other prediction models.