Of cells and cities: a comparative Econometric and Cellular Automata approach to Urban Growth Modeling

This paper presents a comparative assessment of two distinct urban growth modeling approaches. The first urban model uses a traditional Cellular Automata methodology, based on Markov transition chains to prospect probabilities of future urban change. Drawing forth from non-linear cell dynamics, a multi-criteria evaluation of known variables prospects the weights of variables related to urban planning (road net- works, slope and proximity to urban areas). The latter model, frames a novel approach to urban growth modeling using a linear Logit model (LLM) which can account for region specific variables and path depen- dency of urban growth. Hence, the drivers and constraints for both models are used similarly and the same study area is assessed. Both models are projected in the segment of Faro-Olh ̃ao for 2006 and a comparative assessment to ground truth is held. The calculation of Cohenââ¬â¢s Kappa for both projections in 2006 allows for an assessmentof both models. This instrumental approach illuminates the differ- ences between the traditional model and the new type of urban growth model which is used. Both models behave quite differently: While the Markov Cellular Automata model brings an over classification of ur- ban growth, the LLM responds in the underestimation of urban sprawl for the same period. Both excelled with a Kappa calculation of over 89%, and showed to have fairly good estimations for the study area. One may conclude that the Markov CA Model permits a riper un- derstanding of urban growth, but fails to analyze urban sprawl. The LLM model shares interesting results within the possibility of identi- fying urban sprawl patterns, and is therefore an interesting solution for some locations. Another advantage of the LLM is directly linked to the possibility of establishing probability for urban growth. Thus, while the traditional methodology shared better results, LLM can be also an interesting estimate for urban patterns from an econometric perspective. Hence further research is needed in exploring the utility of spatial econometric approaches to urban growth.

Impaired H-Reflex Adaptations Following Slope Walking in Individuals With Post-stroke Hemiparesis

Background and Purpose: Short term adaptations in the Ia afferent-motoneuron pathway, as measured using the H-reflex, in response to altered ground reaction forces (GRFs) applied at the feet during slope walking have been observed in the non-impaired nervous system. The ability of the stroke-impaired nervous system to adapt to altered GRFs have not been examined. The purpose of this study was to examine the acute effects of altered propulsive and braking forces applied at the feet, which naturally occurs when walking on different slopes, on adaptations of the H-reflex pathway in individuals with chronic post-stroke hemiparesis

Neuromuscular Adaptations Following Slope Walking in Individuals Post-Stroke

Background: The excitability of the H-reflex pathway in the non-impaired nervous system can be augmented by altering the different parameters of a walking task, specifically slope. We sought to examine the adaptations in soleus H-reflex excitability and foot force control following an acute bout of upslope or downslope treadmill walking in people post-stroke compared to those who are non- impaired. Methods: We recruited 12 individuals with chronic post-stroke hemiparesis and 9 age-similar non- neurologically impaired individuals. Each subject was tested over 2 sessions separated by at least 7 days. For each session, subjects walked at a self-selected walking speed on an instrumented treadmill for 20 minutes under a level and then an upslope condition, or a level and then a downslope condition, with at least an hour rest between the conditions. The vertical component of ground reaction force was used to determine the stance and swing phase of the gait cycle. Peak propulsion and braking forces were analyzed offline for the first (T1) and last minute (T20) of each walking condition to examine adaptations in foot force control. Soleus H-reflexes (Hmax) were tested before and after each walking condition in the paretic legs of the post-stroke group and the right legs of the control group. To ensure consistency, a control M wave (Mmax) preceding the Hmax was kept constant across all conditions for each subject. Peak to peak amplitudes of the maximal H-reflexes and maximal M waves were measured offline and expressed as an Hmax/Mmax ratio. Results: The paretic legs generated higher propulsion force during upslope (11.75±1.04 %BW), but comparable propulsion forces during downslope, when compared to level walking (6.14±0.67 %BW). However, we did observe statistical significance in main effect for slope in paretic (F(2,22)=33.178, p\u3c0.001), non-paretic (F(1.144, 12.585)=23.246, p\u3c0.001) and non-impaired legs (F(1.137, 10.998)=22.766, p\u3c0.001). Pairwise comparisons between slope types indicated that on average, peak braking forces were higher when walking downslope and lower when walking upslope, when compared to level walking. We observed an overall change in Hmax/Mmax ratio following 20 minutes of walking, and the change was different for post-stroke compared to control group, as suggested by the significant interaction between time and group (F(1,19)=16.84, p=0.001). Conclusion: Our observations suggest that when the biomechanics of the walking task is altered, through adjusting the slope of the walking surface, paretic legs exhibit increased propulsion forces during upslope walking. Paretic propulsive forces were greatest in the upslope condition and lowest in the downslope condition. Regardless of group, individuals had greatest braking forces during the downslope condition and lowest during the upslope condition. We believe, based on current studies, that increased paretic propulsion forces in the upslope condition may be due to the increased difficulty of the environmental condition. In the level condition, spinal circuits in the stroke-impaired nervous system are trending towards adaptations similar to the non-impaired nervous system, such that the Hmax/Mmax ratios were depressed. However, in the more challenging upslope condition, adaptations of the paretic soleus H-reflexes were impaired such that the Hmax/Mmax ratios were trending towards elevated. Future studies will examine the optimal walking duration and degree of slope to induce neural adaptations, as well as determine any long-term retention of plasticity

Of cells and cities: a comparative Econometric and Cellular Automata approach to Urban Growth Modeling

This paper presents a comparative assessment of two distinct urban growth modeling approaches. The first urban model uses a traditional Cellular Automata methodology, based on Markov transition chains to prospect probabilities of future urban change. Drawing forth from non-linear cell dynamics, a multi-criteria evaluation of known variables prospects the weights of variables related to urban planning (road net- works, slope and proximity to urban areas). The latter model, frames a novel approach to urban growth modeling using a linear Logit model (LLM) which can account for region specific variables and path depen- dency of urban growth. Hence, the drivers and constraints for both models are used similarly and the same study area is assessed. Both models are projected in the segment of Faro-Olh ̃ao for 2006 and a comparative assessment to ground truth is held. The calculation of Cohen's Kappa for both projections in 2006 allows for an assessmentof both models. This instrumental approach illuminates the differ- ences between the traditional model and the new type of urban growth model which is used. Both models behave quite differently: While the Markov Cellular Automata model brings an over classification of ur- ban growth, the LLM responds in the underestimation of urban sprawl for the same period. Both excelled with a Kappa calculation of over 89%, and showed to have fairly good estimations for the study area. One may conclude that the Markov CA Model permits a riper un- derstanding of urban growth, but fails to analyze urban sprawl. The LLM model shares interesting results within the possibility of identi- fying urban sprawl patterns, and is therefore an interesting solution for some locations. Another advantage of the LLM is directly linked to the possibility of establishing probability for urban growth. Thus, while the traditional methodology shared better results, LLM can be also an interesting estimate for urban patterns from an econometric perspective. Hence further research is needed in exploring the utility of spatial econometric approaches to urban growth