Test the Effectiveness of the Open Spaces Scenario in Promoting Socio-economic Development

2020 ◽  
Author(s):  
Si Chen ◽  
Zipan Cai ◽  
Brian Deal
Keyword(s):  
Machine Learning ◽  
Economic Development ◽  
Open Space ◽  
Social Factor ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Data Availability ◽  
Open Spaces ◽  
Positive Effects ◽  
Socio Economic Development

<p>The preservation of open spaces is treated as an important policy in recent years as urbanization level is increasing higher in the world (Geoghegan, 2002). There are multiple positive effects associated with open spaces, including recreation, aesthetic and environment values (Geoghegan, 2002). The positive effects of open space as a nature-based solution on urban social, economic and environmental factors have been explored by a number of previous papers, such as housing price (Lutzenhiser & Netusil, 2001; Bolitzer & Netusil, 2000), spatial pattern (Lewis et al., 2009; Irwin & Bockstael, 2004), human health (Groenewegen et al., 2006; Irvine et al., 2013) and social safety (Groenewegen et al., 2006; Fischer et al., 2004). However, relatively less papers have predicted the open spaces’ influences on socio-economic development. This paper will firstly verify the open space influences on economic factor (housing sale prices) and social factor (sense of safety, residential agglomeration) using a linear regression model. We consider the housing attributes, urban form attributes (eg. population density, block size, road density), driving and walking accessibility to different types of public open spaces, and accessibility to other amenities (eg. hospitals and schools) as influential features. Then, we test several machine learning algorithms in predicting the housing price and sense of safety change based on future open space planning scenarios, and choose the most suitable machine learning algorithm. City of Chicago, Illinois, US is chosen to be study area since data availability, sufficient open space types and long-term open space preservation strategies. This study can quantify the values of the open spaces in influencing socio-economic developments and provide a way to test the open space scenarios. It has potential to work as a tool for local planners to make better nature-based solutions in open space designs and plans.</p>

scholarly journals The Neighborhood Effects of a Place-Based Policy—Causal Evidence from Atlanta’s Economic Development Priority Areas

2021 ◽  
Vol 13 (12) ◽  
pp. 6808
Author(s):  
Yuxi Luo ◽  
Zhaohua Zhang ◽  
Jun Zheng ◽  
Diane Hite
Keyword(s):  
Economic Development ◽  
Low Income ◽  
Neighborhood Effects ◽  
Economic Status ◽  
Housing Prices ◽  
Labor Demand ◽  
Housing Price ◽  
Poverty Rate ◽  
Priority Areas ◽  
Positive Effects

Place-based policies refer to government efforts to enhance the economic performance of an area within its jurisdiction. Applying various difference in differences strategies, this study evaluates the neighborhood effects of a place-based policy—the Economic Development Priority Areas (EDPA) of Atlanta, Georgia, USA. Since the census block groups are locally defined and the boundaries may change over time, we defined the neighborhoods by creating a set of 0.25-mile- diameter circles evenly distributed across Atlanta, and used the created buffers as the comparison unit. The empirical estimates showed that EDPA designation significantly reduced poverty rate and increased housing price of EDPA neighborhoods but had no beneficial effects on population size and employment rate. The heterogeneous analysis with respect to different initial economic status of the neighborhoods showed a relative larger and significant effect of EDPA designation on low-income neighborhoods. The increasing labor demand induced by EDPA designation in low-income neighborhoods attracted more population to migrate in and put upward pressure on housing prices. The estimation results are robust when replacing the 0.25-mile-diameter circle neighborhoods with 0.5-mile-diameter circle neighborhoods. Although we found some positive effects of the EDPA program in Atlanta, it would be misguided to assume similar effects occur in other areas implementing place-based policies.

scholarly journals Boon or Bane: Effect of Adjacent YIMBY or NIMBY Facilities on the Benefit Evaluation of Open Spaces or Cropland

2021 ◽  
Author(s):  
Wun-Jheng Wu ◽  
Pei-Ing Wu ◽  
Je-Liang Liou
Keyword(s):  
Open Space ◽  
Housing Price ◽  
Open Spaces ◽  
Urban Open Space ◽  
Individual Housing ◽  
Public Facilities ◽  
Benefit Evaluation ◽  
The City ◽  
Total Damage ◽  
Current Arrangement

This is the first study to comprehensively evaluate the benefit of urban open spaces and cropland with different adjacent public facilities seen as locally undesirable (“not in my backyard,” NIMBY) or desirable (“yes in my backyard,” YIMBY). The total benefit increases or decreases for urban open space and cropland with adjacent NIMBY or YIMBY facilities in a municipality in Taiwan. The results show that for the city as a whole, the current arrangement of NIMBY and YIMBY in different zones decreases the total benefit of urban open spaces in highly urbanized zones and increases the total damage to cropland in extremely rural zones. This indicates a need to avoid further installing NIMBY or YIMBY facilities in already occupied urban open spaces. The results also demonstrate that locating NIMBY or YIMBY facilities near cropland fails to highlight the benefit of YIMBY facilities and magnifies opposition to NIMBY facilities. For individual housing units, the total damage is 1.87% of the average housing price for cropland-type open space with adjacent NIMBY or YIMBY facilities, and the total benefit is 7.43% of the average housing price for urban-type open space in a highly urbanized area. In contrast, the total benefit for open space with adjacent NIMBY or YIMBY facilities is a 2.95%-13.80% increase in the average housing price for areas with mixed urban open space and cropland.

Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms

2011 ◽  
Vol 30 (2) ◽  
pp. 19-50 ◽  
Author(s):  
Johan Perols
Keyword(s):  
Machine Learning ◽  
Analyst Forecasts ◽  
Machine Learning Algorithms ◽  
Data Availability ◽  
Financial Statement ◽  
Support Vector ◽  
Classification Algorithms ◽  
Employee Productivity ◽  
Financial Statement Fraud ◽  
Fraud Risk

SUMMARY This study compares the performance of six popular statistical and machine learning models in detecting financial statement fraud under different assumptions of misclassification costs and ratios of fraud firms to nonfraud firms. The results show, somewhat surprisingly, that logistic regression and support vector machines perform well relative to an artificial neural network, bagging, C4.5, and stacking. The results also reveal some diversity in predictors used across the classification algorithms. Out of 42 predictors examined, only six are consistently selected and used by different classification algorithms: auditor turnover, total discretionary accruals, Big 4 auditor, accounts receivable, meeting or beating analyst forecasts, and unexpected employee productivity. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models. Data Availability: A list of fraud companies used in this study is available from the author upon request. All other data sources are described in the text.

scholarly journals Machine Learning Housing Price Prediction in Petaling Jaya, Selangor, Malaysia

2019 ◽  
Vol 8 (2S11) ◽  
pp. 542-546
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Selling Price ◽  
Prediction Problem ◽  
Real Dataset ◽  
Price Prediction

This paper demonstrates the utilization of machine learning algorithms in the prediction of housing selling prices on real dataset collected from the Petaling Jaya area, Selangor, Malaysia. To date, literature about research on machine learning prediction of housing selling price in Malaysia is scarce. This paper provides a brief review of the existing machine learning algorithms for the prediction problem and presents the characteristics of the collected datasets with different groups of feature selection. The findings indicate that using irrelevant features from the dataset can decrease the accuracy of the prediction models.

scholarly journals Boon or Bane: Effect of Adjacent YIMBY or NIMBY Facilities on the Benefit Evaluation of Open Spaces or Cropland

2021 ◽  
Vol 13 (7) ◽  
pp. 3998
Author(s):  
Wun-Jheng Wu ◽  
Pei-Ing Wu ◽  
Je-Liang Liou
Keyword(s):  
Open Space ◽  
Housing Price ◽  
Open Spaces ◽  
Urban Open Space ◽  
Individual Housing ◽  
Public Facilities ◽  
Benefit Evaluation ◽  
The City ◽  
Total Damage ◽  
Current Arrangement

This is the first study to comprehensively evaluate the benefit of urban open spaces and cropland with different adjacent public facilities seen as locally undesirable (“not in my backyard”, NIMBY) or desirable (“yes in my backyard”, YIMBY). The total benefit increases or decreases for urban open space and cropland with adjacent NIMBY or YIMBY facilities in a municipality in Taiwan. The results show that for the city as a whole, the current arrangement of NIMBY and YIMBY in different zones decreases the total benefit of urban open spaces in highly urbanized zones and increases the total damage to cropland in extremely rural zones. This indicates a need to avoid further installing NIMBY or YIMBY facilities in already occupied urban open spaces. The results also demonstrate that locating NIMBY or YIMBY facilities near cropland fails to highlight the benefit of YIMBY facilities and magnifies opposition to NIMBY facilities. For individual housing units, the total damage is 1.87% of the average housing price for cropland-type open space with adjacent NIMBY or YIMBY facilities, and the total benefit is 7.43% of the average housing price for urban-type open space in a highly urbanized area. In contrast, the total benefit for open space with adjacent NIMBY or YIMBY facilities is a 2.95–13.80% increase in the average housing price for areas with mixed urban open space and cropland.

scholarly journals Housing-Price Prediction in Colombia using Machine Learning

2021 ◽  
Author(s):  
MIGUEL ANGEL CORREA MANRIQUE ◽  
Omar Becerra Sierra ◽  
Daniel Otero Gomez ◽  
Henry Laniado ◽  
Rafael Mateus C ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evaluation Studies ◽  
Housing Prices ◽  
House Price ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Learning Tools ◽  
Price Prediction ◽  
Statistical Indicator ◽  
Real Estate Company

It is a common practice to price a house without proper evaluation studies being performed for assurance. That is why the purpose of this study provide an explanatory model by establishing parameters for accuracy in interpretation and projection of housing prices. In addition, it is intentioned to establish proper data preprocessing practices in order to increase the accuracy of machine learning algorithms. Indeed, according to our literature review, there are few articles and reports on the use of Machine Learning tools for the prediction of property prices in Colombia. The dataset in which the research is built upon was provided by an existing real estate company. It contains near 940,000 items (housing advertisements) posted on the platform from the year 2018 to 2020. The database was enriched using statistical imputation techniques. Housing prices prediction was performed using Decision Tree Regressors and LightGBM methods, thus deriving in better alternatives for house price prediction in Colombia. Moreover, to measure the accuracy of the proposed models, the Root Mean Squared Logarithmic Error (RMSLE) statistical indicator was used. The best cross validation results obtained were 0.25354±0.00699 for the LightGBM, 0.25296 ±0.00511 for the Bagging Regressor, and 0.25312±0.00559 for the ExtraTree Regressor with Bagging Regressor, and it was not found a statistical difference between their performances.

scholarly journals Estimating the Distributed Generation Unit Sizing and Its Effects on the Distribution System by Using Machine Learning Methods

2021 ◽  
Author(s):  
Mikail Purlu ◽  
Belgin Emre Turkay
Keyword(s):  
Machine Learning ◽  
Distributed Generation ◽  
Distribution System ◽  
Distribution Systems ◽  
Load Flow ◽  
Machine Learning Algorithms ◽  
Estimation Methods ◽  
Percentage Error ◽  
Support Vector ◽  
Positive Effects

Many approaches about the planning and operation of power systems, such as network reconfiguration and distributed generation (DG), have been proposed to overcome the challenges caused by the increase in electricity consumption. Besides the positive effects on the grid, contributions on environmental pollution and other advantages, the rapid developments in renewable energy technologies have made the DG resources an important issue, however, improper DG allocation may result in network damages. A lot of studies have been practised with analytical and heuristic methods based on load flow for optimal DG integration to the network. This novel method based on estimation is proposed to determine the size of DG and its effects on the network to get rid of the coercive and time-consuming load flow techniques. Machine learning algorithms, such as Linear Regression, Artificial Neural Network, Support Vector Regression, K-Nearest Neighbor, and Decision Tree, have been used for the estimations and have been applied to well-known test systems, such as IEEE 12-bus, 33-bus, and 69-bus distribution systems. The accuracy of the proposed estimation methods has been verified with R-squared and mean absolute percentage error. Results show that the proposed DG allocation method is effective, applicable, and flexible.

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