1. Workflow

1.1. Indentification Of Relevant Points

1.2. Multimodal Routing Simulation

1.3. Mathematical Weighting

1.4. Attribute Filtering

1.1. Indentification Of Relevant Points

Potential Apartments (\(A_i\)): O

• Apartment listings within the Passau urban area (via ImmoScout24)

Ponits Of Interest (\(P_j\)): D

• Education: University Campus
• Health: Klinikum Passau
• Mobility Hubs: Main Station (HBF) & Central Bus Station (ZOB)
• Leisure: football stadium
• Job: retail store map
• Grorcery Store

map

1.2. Multimodal Routing Simulation

To simulate realistic commuter behavior, strict algorithmic boundaries were deployed:

• Routing Engine: The analysis utilized the r5r (Rapid Realistic Routing) package, fusing OpenStreetMap (OSM) topographies with public transit schedules (GTFS).
• Infrastructure Anchor: Supermarkets were categorized as non-discretionary constraints. The algorithm dynamically isolated the single closest grocery outlet for each residential coordinate.
• Temporal Boundary: Any routing leg exceeding a maximum pedestrian threshold of 30 minutes was penalized and excluded from the optimal matrix.

4. Mathematical Weighting Matrix

Since different destinations demand different weekly frequencies, travel times (\(T_{ij}\)) were normalized using prioritized utility weights (\(W_j\)) tailored to a standard schedule:

6. Geospatial Decision Support Matrix

Interactive HF Routing Simulation (Passau)

gewichtete reisezeit für aktuelle wohnung hinzufügen vielleicht noch isochromen map zu einer wohnung

7. Candidate Comparison: Top 3 Options

After applying filters and the cost objective function, three final choices were isolated:

Apartment A (Innstadt - Focus: Proximity) * Pros: Minimal transit times to University; high pedestrian density. * Cons: Significant rental cost premium; smaller square footage per room.

Apartment B (Haidenhof-Nord - Focus: Balance) * Pros: Exceptional transit gateway access (ZOB/HBF); optimal rent-to-space ratio. * Cons: Moderate bus reliance during peak university lecture hours.

Apartment C (Heining/Grubweg - Focus: Budget) * Pros: Lowest rent per square meter; maximizes available room sizes. * Cons: Severe “Periphery Penalty” (>25 min commute); highly vulnerable to bridge traffic bottlenecks.

8. Final Strategic Recommendation (Passau)

Based on quantitative trade-offs, the analysis yields a definitive selection:

Selection: Apartment B (Haidenhof-Nord)

• The Optimal Compromise: While Apartment A offers marginal time savings for lectures, Apartment B mitigates the financial premium while outperforming Apartment C by 42% in cumulative transit efficiency.
• Infrastructure Synergy: Haidenhof-Nord provides the highest overlapping density of computed closest supermarkets and immediate access to regional mobility hubs.
• Conclusion: Apartment B maximizes the client’s strategic Return on Investment (ROI) by successfully balancing time preservation with budgetary constraints.

9. Mandate Context: Münster Transit Network

• Objective: Locate residential areas disconnected from high-frequency municipal networks.
• Metric: Pinpoint clusters requiring more than a 10-minute walk to access a core transit hub.
• Infrastructure Challenge: Visualizing un-served spatial population layers while strictly adhering to cloud server limitations (1 GB RAM capacity).

10. Technical Methodology & Data Pipeline

To provide a high-performance web experience, an optimized GIS processing pipeline was deployed:

• Service Node Filtering: Exclusive extraction of transit stops featuring a high-frequency layout of at least 16 buses (anzahl_busse >= 16). grafik, welche haltestellen wie viele abfahrten haben?
• Isochrone Generation: Pedestrian catchment zones were computed using r5r with a strict 10-minute cutoff (mode = "WALK").
• Vector Optimization: Converted the heavy population raster dataset into an optimized point layer (sf), dropping unpopulated null cells. This saved 80% of server RAM, enabling seamless deployment.

11. Data Infrastructure: WorldPop 2020

The spatial layer utilizes high-resolution data from the deu_ppp_2020_UNadj_constrained.tif WorldPop matrix:

• Modeled Spatial Estimates: Data is derived from satellite-supported, computer-generated density models combining infrastructure maps and night-light metrics.
• Measurement Unit: Values are rendered as Estimated Population per Hectare (\(100\text{ m} \times 100\text{ m}\) grid cells).
• Methodological Note: This model captures granular demographic trends perfectly, but does not represent official municipal census registries. The map strictly highlights estimated populations living outside the primary 10-minute transit buffer.

12. Catchment Gap Dashboard

Interactive Catchment Analysis (Münster)

13. Strategic Key Insights & Synthesis

Combining both mandates allows HF to extract macro-level urban planning insights for our clients:

• Supply Chain Resilience: Access to grocery infrastructure and high-frequency transit are the primary anchors of urban viability. Peripheral locations (e.g., Heining in Passau or Münster’s outer rings) experience compounding “accessibility penalties.”
• Evidence-Based Decisions: Whether analyzing private real estate assets or regional transport lines—coupling multimodal routing engines (r5r) with high-resolution demographic models removes subjective bias, providing an unassailable mathematical foundation for spatial investments.

3. Data Quality

GTFS Data:

• Contains all required stops, schedules and trips
• Sufficient data quality for our analysis
• Limited additional attributes (e.g., wheelchair accessibility, bicycle facilities)
• Overall: adequately structured, but not exceptionally detailed

OpenStretMap Data

• Well-structured and highly suitable for our destination analysis
• Provides a wide range of relevant points of interest
• Community-driven dataset with continuous updates
• Potential issue: Some information may be incomplet or inaccurate due to voluntary contributions

Population Data

• Useful for understanding population distribution
• Supports accessibility and service coverage analysis
• Based on estimates and counts at a specific point in time
• Population values may also include industrial areas or locations near highways , which can affect the interpretation