scholarly journals Utilizing Urban Geospatial Data to Understand Heritage Attractiveness in Amsterdam

2021 ◽  
Vol 10 (4) ◽  
pp. 198
Author(s):  
Sevim Sezi Karayazi ◽  
Gamze Dane ◽  
Bauke de Vries
Keyword(s):  
Geographically Weighted Regression ◽  
Spatial Clustering ◽  
Least Square ◽  
Geospatial Data ◽  
Evidence Based ◽  
Ordinary Least Square ◽  
Mass Tourism ◽  
Dbscan Algorithm ◽  
National Monuments ◽  
Combined Data

Touristic cities are home to historical landmarks and irreplaceable urban heritages. Although tourism brings financial advantages, mass tourism creates pressure on historical cities. Therefore, “attractiveness” is one of the key elements to explain tourism dynamics. User-contributed and geospatial data provide an evidence-based understanding of people’s responses to these places. In this article, the combination of multisource information about national monuments, supporting products (i.e., attractions, museums), and geospatial data are utilized to understand attractive heritage locations and the factors that make them attractive. We retrieved geotagged photographs from the Flickr API, then employed density-based spatial clustering of applications with noise (DBSCAN) algorithm to find clusters. Then combined the clusters with Amsterdam heritage data and processed the combined data with ordinary least square (OLS) and geographically weighted regression (GWR) to identify heritage attractiveness and relevance of supporting products in Amsterdam. The results show that understanding the attractiveness of heritages according to their types and supporting products in the surrounding built environment provides insights to increase unattractive heritages’ attractiveness. That may help diminish the burden of tourism in overly visited locations. The combination of less attractive heritage with strong influential supporting products could pave the way for more sustainable tourism in Amsterdam.

scholarly journals Geographically varying relationships of COVID-19 mortality with different factors in India

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Asif Iqbal Middya ◽  
Sarbani Roy
Keyword(s):  
Geographically Weighted Regression ◽  
Information Criterion ◽  
Driving Factors ◽  
Least Square ◽  
Weighted Regression ◽  
Related Factors ◽  
Local Method ◽  
Ordinary Least Square ◽  
Local Methods ◽  
Global And Local

AbstractCOVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ($$R^{2}=0.97$$ R 2 = 0.97 ) with smaller Akaike Information Criterion (AICc $$=-66.42$$ = - 66.42 ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s $$I=-0.0395$$ I = - 0.0395 and $$p < 0.01$$ p < 0.01 ) in the residuals. It is found that more than 86% of local $$R^{2}$$ R 2 values are larger than 0.60 and almost 68% of $$R^{2}$$ R 2 values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found.

scholarly journals Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

2019 ◽  
Vol 11 (11) ◽  
pp. 3220 ◽  
Author(s):  
Fan Yang ◽  
Fan Ding ◽  
Xu Qu ◽  
Bin Ran
Keyword(s):  
Geographically Weighted Regression ◽  
Least Square ◽  
Weighted Regression ◽  
Transportation Mode ◽  
Ordinary Least Square ◽  
Social Network Data ◽  
Point Of Interest ◽  
The Relationship ◽  
Excessive Number

Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential in estimating the reasonable size of shared-bike fleet. This paper proposed a methodology to estimate the shared-bike trip using location-based social network data and conducted a case study in Nanjing, China. The ordinary least square, geographically weighted regression (GWR) and semiparametric geographically weighted regression (SGWR) methods are used to establish the relationship among shared-bike trip, distance to the subway station and check ins in different categories of the point of interest (POI). This method could be applied to determine the reasonable number of shared-bikes to be launched in new places and economically benefit in shared-bike management.

scholarly journals Geographically varying relationships of COVID-19 mortality with different factors in India

2020 ◽  
Author(s):  
Asif Iqbal Middya ◽  
Sarbani Roy
Keyword(s):  
Geographically Weighted Regression ◽  
Information Criterion ◽  
Driving Factors ◽  
Least Square ◽  
Weighted Regression ◽  
Related Factors ◽  
Local Method ◽  
Ordinary Least Square ◽  
Local Methods ◽  
Global And Local

Abstract COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance (R2 = 0:973) with smaller Akaike Information Criterion (AICc = -77:93) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s I = -0.0436 and p < 0:01) in the residuals. It is found that more than 87.5% of local R2 values are larger than 0.60 and almost 60% of R2 values are within the range 0:80 - 0:97. Moreover, some interesting local variations in the relationships are also found.

Dampak Kebijakan Moneter Pada Output Di Negara-Negara ASEAN Tahun 1980-2014

2017 ◽  
Vol 4 (1) ◽  
pp. 58-70
Author(s):  
Nur Widiastuti
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Fixed Effects ◽  
Empirical Studies ◽  
Negative Relationship ◽  
Least Square ◽  
Fixed Effects Model ◽  
State Data ◽  
Ordinary Least Square ◽  
The Impact

The Impact of monetary Policy on Ouput is an ambiguous. The results of previous empirical studies indicate that the impact can be a positive or negative relationship. The purpose of this study is to investigate the impact of monetary policy on Output more detail. The variables to estimatate monetery poicy are used state and board interest rate andrate. This research is conducted by Ordinary Least Square or Instrumental Variabel, method for 5 countries ASEAN. The state data are estimated for the period of 1980 – 2014. Based on the results, it can be concluded that the impact of monetary policy on Output shown are varied.Keyword: Monetary Policy, Output, Panel Data, Fixed Effects Model

PENGARUH PENANAMAN MODAL ASING, PERDAGANGAN INTERNASIONAL, GDP PER KAPITA, SEKTOR PERTANIAN, DAN URBANISASI TERHADAP PENDUDUK BEKERJA DI INDONESIA

2017 ◽  
Vol 21 (2) ◽  
pp. 85-95
Author(s):  
John Marcell Rumondor
Keyword(s):  
International Trade ◽  
Gross Domestic Product ◽  
Foreign Investment ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Least Square ◽  
Domestic Product ◽  
Working Population ◽  
Ordinary Least Square ◽  
Per Capita

This research aims to understand the influenceof foreign investment, international trade, Gross Domestic Product per capita, agriculture and urbanization of the working population. Country used as an object in this research is Indonesia. This research uses the method of analysis Ordinary Least Square (OLS) and the multiple linear regression analysis method. Research period are from 1997 – 2012. The results showed that the international trade, Gross Domestic Product per capita, agriculture and urbanization have significantpositive influenceon the population work in Indonesia, but foreign investment has no significanteffect on the working population in Indonesia.

PENGARUH KNOWLEDGE MANAGEMENT, KOMPETENSI, MOTIVASI TERHADAP KINERJA KARYAWAN

2015 ◽  
Vol 5 (2) ◽  
pp. 1
Author(s):  
Miftahol Arifin
Keyword(s):  
Knowledge Management ◽  
Linear Regression ◽  
Significant Influence ◽  
Employee Performance ◽  
Least Square Method ◽  
Least Square ◽  
Analysis Tool ◽  
Ordinary Least Square ◽  
Competence Motivation ◽  
Performance Analyze

The purpose of this research is to analyze the influence of knowledge management on employee performance, analyze the effect of competence on employee performance, analyze the influence of motivation on employee performance). In this study, samples taken are structural employees PT.centris Kingdom Taxi Yogyakarta. The analysis tool in this study using multiple linear regression with Ordinary Least Square method (OLS). The conclusion of this study showed that the variables of knowledge management has a significant influence on employee performance, competence variables have an influence on employee performance, motivation variables have an influence on employee performance, The analysis showed that the variables of knowledge management, competence, motivation on employee performance.Keywords: knowledge management, competence, motivation, employee performance.

The Impact of Competitive Quasi-Market on Service Delivery in Benue State University, Makurdi Nigeria

GIS Business ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. 85-98
Author(s):  
Idoko Peter
Keyword(s):  
Service Delivery ◽  
A Priori ◽  
Least Square ◽  
Secondary Source ◽  
State University ◽  
Ordinary Least Square ◽  
Negative Effect ◽  
The Impact ◽  
The Relationship

This research the impact of competitive quasi market on service delivery in Benue State University, Makurdi Nigeria. Both primary and secondary source of data and information were used for the study and questionnaire was used to extract information from the purposively selected respondents. The population for this study is one hundred and seventy three (173) administrative staff of Benue State University selected at random. The statistical tools employed was the classical ordinary least square (OLS) and the probability value of the estimates was used to tests hypotheses of the study. The result of the study indicates that a positive relationship exist between Competitive quasi marketing in Benue State University, Makurdi Nigeria (CQM) and Transparency in the service delivery (TRSP) and the relationship is statistically significant (p<0.05). Competitive quasi marketing (CQM) has a negative effect on Observe Competence in Benue State University, Makurdi Nigeria (OBCP) and the relationship is not statistically significant (p>0.05). Competitive quasi marketing (CQM) has a positive effect on Innovation in Benue State University, Makurdi Nigeria (INVO) and the relationship is statistically significant (p<0.05) and in line with a priori expectation. This means that a unit increases in Competitive quasi marketing (CQM) will result to a corresponding increase in innovation in Benue State University, Makurdi Nigeria (INVO) by a margin of 22.5%. It was concluded that government monopoly in the provision of certain types of services has greatly affected the quality of service experience in the institution. It was recommended among others that the stakeholders in the market has to be transparent so that the system will be productive to serve the society effectively

Peranan Sektor Pertanian dalam Perekonomian Kabupaten Manokwari

Agrotek ◽  
2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Kunto Wibowo
Keyword(s):  
Agricultural Sector ◽  
Raw Materials ◽  
Secondary Data ◽  
Least Square ◽  
Food Crops ◽  
Literature Study ◽  
Location Quotient ◽  
Ordinary Least Square ◽  
Plantation Crops ◽  
Vegetables And Fruits

<em>The agricultural sector</em><em> is a strategic sector in Manokwari regency. The agricultural sector provides a major contribution in the regional economy, an economic base of rural people, dominate the life of most residents in this region and provider of food and raw materials for other sectors. The purpose of this study was to determine how big the contribution of different sub-sectors that exist in the agricultural sector, which analyzes sectors influential in changing the economic structure of agriculture in the area and know the potential commodities that can be developed in an effort to enhance the role of the agricultural sector. The research method used through literature study and analysis of secondary data sourced from the relevant authorities. To find out how big the factors that influence changes in economic structures of domination of the agricultural sector into non-agricultural sector estimates used Ordinary Least Square (OLS). For the determination of the potential commodities that can be seeded used method approach Location Quotient (LQ). The results showed the greatest contribution of the different sub-sectors within the agricultural sector contained in the food crops sub-sector. Based on the rate of growth per year, plantation crops sub-sector occupied the highest positions. The sectors that provide real impact on the agricultural sector's contribution to the regional gross domestic product �of the building sector and services sector. Potential commodities that can be developed in different areas in Manokwari regency include food crops and pulses, vegetables and fruits and livestock including cows, goats, pigs and chicken.</em>

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