Monitoring habitat types by the mixed multinomial logit model using panel data

Brus, D.J.; Slim, P.A.; Gort, Gerrit; Heidema, A.H.; Dobben, H.F. van

doi:10.1016/j.ecolind.2016.02.043

Cited by 6 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was found that the Pseudo-R 2 of MMNL is bigger (0.2586). Brus et al (2016) monitored the changes in habitat types by producing sequential maps based on point information followed by mapping using a multinomial logit regression model with abiotic variables. The results showed that the MMNL model fitted significantly better than the MNL model with the same fixed effects.…”

Section: Resultsmentioning

confidence: 99%

Comparison of the Models in Choice Experiments Method Application for Watershed Afforestation in Southern Turkey

Deniz

Giergiczny

Riera³

et al. 2020

Kastamonu Üniversitesi Orman Fakültesi Dergisi

View full text Add to dashboard Cite

Aim of study: In this study, the value estimates of the four benefits (preventing floods, reducing soil erosion, increasing dam life and access to quality spring water) created by erosion control activities are compared according to different models and the determination of these models.Area of study: The Çakıt Stream Watershed was selected as a study area. Material and methods: In this study, the data collected by Deniz ( 2012) to apply Choice Experiments Method were used. While Deniz (2012) had estimated the marginal values of the benefits based on Multinomial Logit Model, in this study, the estimations were separately made in order to compare the results of Deniz ( 2012) using both another Multinomial Logit Model with different variables and Mixed Multinomial Logit Model. Main results: Although the marginal values found by using Mixed Multinomial Logit Model are different from the estimations found with Multinomial Logit Model, the relationships among the variables did not change. Determination of Mixed Multinomial Logit Model is bigger (Pseudo-R 2 =0.548) than Multinomial Logit Models (0.251 and 0.071).Highlights: In the study, it was observed that as the models used to determine the value of the erosion control service changed, the magnitude of the estimated values also changed.

show abstract

Section: Resultsmentioning

confidence: 99%

Comparison of the Models in Choice Experiments Method Application for Watershed Afforestation in Southern Turkey

Deniz

Giergiczny

Riera³

et al. 2020

Kastamonu Üniversitesi Orman Fakültesi Dergisi

View full text Add to dashboard Cite

show abstract

“…Here, three decades of natural gas extraction has resulted in soil subsidence, which has impacted vegetation structure and habitats. In addition to vegetation plot recordings to track changes in species composition (Van Dobben and Slim, 2012; Brus et al, 2014Brus et al, , 2016, wider spatial changes in vegetation structure must be monitored. Mapping of vegetation structure is also important for species identification and distribution modelling, since fauna and flora often have strong preferences for specific vegetation niches (Bunce et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Exploiting low-cost and commonly shared aerial photographs and LiDAR data for detailed vegetation structure mapping of the Wadden Sea island of Ameland

Mücher

Kramer

Najafabadi³

et al. 2019

SDRP-JESES

View full text Add to dashboard Cite

Mücher, C.A. et al., Exploiting low-cost and commonly shared aerial photographs and LiDAR data for detailed vegetation structure mapping of the Wadden Sea island of Ameland (2019) SDRP Journal of Earth Sciences & Environmental Studies 4(1) RESEARCH HIGHLIGHTS 1. Both RB and RF classification methods performed well in vegetation structure mapping, respectively, 84.1% and 86.4%. 2. RF was preferred over RB, since the former was better able to handle the complexity of the rules needed to distinguish many classes. 3. Exploitation of digital aerial photographs in semi-automatic classification processes remains challenging, due to inaccurate calibration of the reflectance values and the limited number of spectral bands in aerial photographs. 4. High resolution satellite imagery is a good alternative if aerial photographs are not available.

show abstract

“…To assess the performance of the predictions made by the model, a goodness-of-fit measure or McFadden's R 2 is calculated. This is a widely accepted method for calculating the goodness-of-fit of a model (Brus et al, 2016;Chiou et al, 2013;Hasnine et al, 2018). The equation to calculate the McFadden 𝑅 2 is as described in section 3.2.1 of this thesis.…”

Section: Model Goodness-of-fitmentioning

confidence: 99%

“…The possible choice set for the chosen NL can be seen in Figure 22. The empirical form of the test is taken from (Hausman & McFadden, 1984) In the context of mode choice analysis with unbalanced choice sets, this hypothesis setting is appropriately specified (Brus et al, 2016;Çelik & Oktay, 2014).…”

Section: Testing the Validity Of The Iia Assumptionmentioning

confidence: 99%

Disaggregate Probabilistic Models to Predict Trip Generation Propensities and Mode Choice Behaviour

Siddiqui¹

View full text Add to dashboard Cite

Travel behaviour models serve as important tools to understand what factors affect trip generation and preferred mode of travel in different contexts. These models can be used to quantify the impacts and assess the consequences of development plans and policy actions. Growing populations and the increase in travel demand warrant investigation into determinants of travel behaviour that can guide policy changes and infrastructure investments. This thesis uses two cross-sectional datasets containing various sociodemographic and land-use attributes from 2015 and 2019. Trip-generation propensities for transit and automobile modes are predicted using a bivariate ordered probit approach which enables the determining of factors affecting the trip-generation propensity of each mode while establishing the correlation between their propensities. Further, a multinomial logit model is estimated to investigate the determinants of mode choice for home-based discretionary trips. The results show that improvement in areas with low transit accessibility can considerably increase the transit trip-making propensity and living in downtown Toronto increases the probability of choosing active modes of travel.

show abstract

Monitoring habitat types by the mixed multinomial logit model using panel data

Cited by 6 publications

References 15 publications

Comparison of the Models in Choice Experiments Method Application for Watershed Afforestation in Southern Turkey

Comparison of the Models in Choice Experiments Method Application for Watershed Afforestation in Southern Turkey

Exploiting low-cost and commonly shared aerial photographs and LiDAR data for detailed vegetation structure mapping of the Wadden Sea island of Ameland

Disaggregate Probabilistic Models to Predict Trip Generation Propensities and Mode Choice Behaviour

Contact Info

Product

Resources

About