Analyzing the Importance Ranking of Spatial Features in Behavioral Responses to Exercise

In a world where urban planning and design increasingly aim to foster healthier lifestyles, the understanding of spatial feature indicators is paramount. This analysis unpacks the results from three prominent machine learning models—Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and XGBoost—focusing on their performance in ranking features influencing specific physical behaviors such as exercising, jogging, sitting, walking, standing, and even playing chess and cards.

Importance Ranking Overview

As we dive into the findings, a consistent trend emerges: the "type of fitness equipment" stands out as the most influential factor across all models, emphasizing its critical role in encouraging exercise behaviors. However, while certain indicators maintain uniformity in importance, others present significant discrepancies, highlighting the nuanced relationships between spatial features and human behavior.

Exercising Behavior

Figures 7 (a), (b), and (c) depict the importance rankings for exercising behavior:

• RF Model: Here, the top four influential indicators include the type of fitness equipment, fitness equipment density, the number of fitness facilities, and spatial scale.

• GBDT Model: Similar results emerge, with the leading indicators being type of fitness equipment, fitness equipment density, enclosure degree, and the number of steps.

• XGBoost Model: The top variables differ slightly, ranking type of fitness equipment, spatial scale, number of fitness facilities, and spatial shape.

Across all three models, the type of fitness equipment consistently ranks first, reinforcing its importance in designing environments that promote exercise.

Jogging Behavior

In contrast, for jogging behaviors illustrated in Figures 8 (a), (b), and (c), commonality shifts:

• RF Model: The number of entrances, spatial shape, and spatial scale take precedence.

• GBDT Model: The ranking holds the number of entrances and spatial shape but incorporates the number of people served as a crucial factor.

• XGBoost Model: The model singularly emphasizes spatial scale as the primary indicator.

Similar interpretations surface regarding the number of entrances and spatial shape, while variability becomes apparent in other featured rankings.

Sitting Behavior

Figures 9 (a), (b), and (c) present insights into sitting behaviors:

• RF Model: Key indicators include the sitting area, number of fitness facilities, green enclosure degree, and spatial scale.

• GBDT Model: The same trio—sitting area, number of fitness facilities, and green enclosure—appears, but adds the number of people served to the mix.

• XGBoost Model: Retains those aforementioned indicators while distinguishing itself with an emphasis on spatial shape.

Here, consistency arises in three out of four indicators across models, showcasing commonalities despite varying interpretations of spatial scale.

Walking Behavior

Moving to Figures 10 (a), (b), and (c) for walking behavior, different yet intriguing results emerge:

• RF Model: Spatial shape, spatial scale, number of entrances, and green enclosure degree rank highest.

• GBDT Model: It prioritizes spatial scale, shape, number of people served, and enclosure degree.

• XGBoost Model: Maintains similar ranks to GBDT, reinforcing the significance of spatial scale and shape.

Again, a blend of consistency and divergence highlights the complex interplay influencing walking behaviors.

Standing and Chess/Card Playing Behavior

As we analyze Figures 11 (a), (b), and (c) regarding standing behavior, we see:

• RF Model: Lists sitting area, spatial shape, spatial scale, and number of fitness facilities as critical indicators.
• GBDT Model: Mirrors the RF results, yet presents spatial shape and scale with almost interchangeable rankings.

Moving to chess and card playing, depicted in Figures 12 (a), (b), and (c):

• RF Model: Prioritizes spatial scale, sitting area, the number of people served, and chess table density.
• GBDT Model: Adopts a similar ranking with the addition of number of steps.

In these sections, spatial scale remains a constant influencer, demonstrating the significance of environmental design in even sedentary activities.

Exploring Nonlinear Relationships

To deepen our understanding of how spatial indicators interact with human behavior, we employed Individual Conditional Expectation (ICE) and Partial Dependence Plots (PDPs). These visualization tools help assess the impact of specific features visually, revealing trends and thresholds in behavior influenced by spatial characteristics.

Exercising and Jogging Behavior

In Figures 13, 14, and 15, we explored exercising indicators against the models, revealing a stepwise increase in the influence of the type of fitness equipment, reflecting a threshold beyond which behavior remains constant.

For jogging behavior, Figures 16 and 17 highlight further non-linear interactions, with the number of entrances presenting a notable threshold effect, influencing detours and accessibility.

Sitting and Walking Behavior

Figures 18, 19, and 20 for sitting behavior showcase the nonlinear relationship of seating area and fitness facilities—initially improving behavior until a saturation point.

Walking behavior, as illustrated in Figures 21, 22, and 23, emphasizes the complexity of spatial scale and shape, underscoring shifts in walking patterns based on available pathways.

Standing and Chess/Card Playing Behavior

Finally, Figures 24, 25, and 26 for standing behavior reveal that while spatial shape exhibits nonlinear characteristics, the spatial scale’s role becomes evident.

Chess and card playing behaviors, as seen in Figures 27, 28, and 29, highlight that while spatial scale initially seems detrimental, the sitting area and number of tables significantly enhance engagement.

Discussing Impact Mechanisms

The interactions reveal critical insights into how spatial feature indicators influence different behaviors. For instance:

• Jogging Behavior: Entrance count influences significantly until plateauing, emphasizing ease of access. The spatial shape showcases a negative relationship at some thresholds, impacting clarity and utility of jogging routes.

• Sitting Behavior: A larger seating area initially encourages more sitting but eventually stabilizes upon meeting demand, while the presence of fitness facilities supports restorative actions post-exercise.

• Walking Behavior: Increasing spatial scale is beneficial until a physical capacity is reached, showcasing how environmental design impacts elderly mobility.

• Chess/Card Playing Behavior: Smaller, cozy environments favor engagement, while excessive scale dilutes intentions.

This rich analysis illustrates that as spatial feature indicators rise or fall within certain limits, their sensitivity to influencing behavior also alters, affirming previous research indicating diminishing returns in behavior outcomes relative to spatial design.

Acknowledging Limitations and Future Directions

While this study offers significant insights, it isn’t without limitations. The reliance on a single data collection time frame restricts a comprehensive understanding of dynamic behavior influenced by seasonal changes. Additionally, the interaction effects of multiple spatial variables warrant further exploration.

Future research should consider a broader temporal scope and delve deeper into synergistic interactions among varying spatial indicators, ultimately enhancing our understanding of how to create more age-friendly environments in urban settings.

This enriched discourse on spatial feature indicators promises to inform better urban landscapes, aligning with a collective vision for healthier communities.