Integrated MCDA Approach For Homestay Suitability Mapping In The Himalayan Terrain Of Uttarkashi District, Uttarakhand

The Indian Himalayan Region (IHR) has witnessed exponential growth in tourism over the past two decades, driven by increasing domestic and international interest in mountain ecosystems, pilgrimage circuits, and adventure tourism [1]. Uttarakhand, often referred to as "Devbhumi" (Land of Gods), attracts millions of tourists annually to destinations such as Gangotri, Yamunotri, Kedarnath, and Badrinath [2]. Within this context, Uttarkashi district holds strategic importance as the gateway to Gangotri and numerous trekking routes. However, the conventional hotel-based accommodation model has proven inadequate and environmentally unsustainable in this fragile mountain landscape. Homestays have emerged as a viable alternative that promotes community-based tourism, cultural exchange, and equitable distribution of economic benefits [3].

The concept of homestays in the Indian Himalayan context goes beyond mere accommodation; it represents a holistic approach to rural development, women's empowerment, and conservation of local traditions [4]. Unlike large-scale hotel infrastructure, homestays have minimal land use footprints, utilize local resources, and generate direct employment for host families. Furthermore, homestay tourism reduces pressure on existing infrastructure, encourages waste management practices at the household level, and fosters deeper visitor appreciation of mountain lifestyles. Despite these advantages, the spatial distribution of homestays in Uttarkashi remains ad hoc, driven by individual initiatives rather than systematic planning [1,5].

Geographically, Uttarkashi presents both opportunities and challenges for homestay development. The district's rugged topography, ranging from river valleys at 1,200 meters to snow-clad peaks exceeding 6,000 meters, creates diverse microclimates and landscape aesthetics [6]. The Bhagirathi river system, dense oak and deodar forests, alpine meadows (bugyals), and proximity to protected areas such as Govind Pashu Vihar National Park and Gangotri National Park offer unique experiential tourism potential. However, the same geographic features impose constraints: steep slopes increase construction costs and landslide risks, high seismic activity demands resilient building practices, and extreme winter temperatures limit tourist seasons in higher elevations [7,8].

Previous studies on homestay suitability have largely focused on socio-economic parameters, often neglecting the physical-environmental determinants that are critical in mountain settings. Moreover, most tourism planning in Uttarakhand has been destination-centric, overlooking the potential of intermediate villages along pilgrimage routes. This research gap necessitates a robust, multi-criteria approach that integrates both physical and locational factors. Multi-Criteria Decision Analysis (MCDA) provides an ideal framework for such complex spatial decisions, as it allows systematic combination of diverse criteria with varying importance levels [9–11].

The specific objectives of this study are: (1) to identify and map potential zones for homestay development in Uttarkashi district using GIS-based MCDA; (2) to rank and weight eleven selected parameters based on their relative significance; (3) to validate the suitability model through field verification; and (4) to provide actionable recommendations for sustainable homestay promotion. The novelty of this research lies in its integration of seismic and geological parameters—often overlooked in tourism suitability studies—which are particularly relevant for the seismically active Garhwal Himalaya [12].

This study is significant from both academic and policy perspectives. Methodologically, it demonstrates the application of AHP-weighted overlay analysis in mountain tourism planning. Practically, the resulting suitability map can guide district authorities, tourism departments, and rural development agencies in targeting subsidies, training programs, and infrastructure development. For local communities, the map identifies villages where homestay enterprises are most likely to succeed, reducing investment risks. Ultimately, by promoting homestays in suitable locations, this research contributes to the larger goals of sustainable mountain development, disaster risk reduction, and climate-resilient tourism [13].

2. STUDY AREA

Uttarkashi district is situated in the western part of Uttarakhand state, India, spanning latitudes 30°17' N to 31°22' N and longitudes 77°40' E to 79°00' E. Covering an area of approximately 8,016 square kilometers, it is the second-largest district in Uttarakhand by area. The district is bounded by Tibet Autonomous Region of China to the north, Rudraprayag and Tehri Garhwal districts to the south, Pithoragarh and Chamoli districts to the east, and Dehradun district to the west. Administratively, the district comprises six tehsils: Barkot, Dunda, Bhatwari, Chinyalisaur, Mori, and Purola [14].

The terrain is predominantly mountainous, with elevations ranging from approximately 1,200 meters above mean sea level in the southern valleys to over 6,800 meters at Mount Jaonli. The Bhagirathi river, a major tributary of the Ganges, flows through the entire length of the district, creating a distinct river valley corridor that has historically been the main settlement and transportation axis. Major tributaries including Assi Ganga, Bhilangna, and Jalandhari Ganga join the Bhagirathi within the district [15].

Climatically, Uttarkashi experiences significant variations with altitude. Lower valleys have sub-tropical to temperate climates (mean annual temperature 15-20°C), while higher reaches above 3,000 meters experience alpine to glacial conditions (mean annual temperature below 5°C). Annual precipitation ranges from 1,000 mm in the rain-shadow interior valleys to over 2,500 mm on south-facing slopes, with approximately 70% received during the southwest monsoon (June-September) and the remainder as winter snowfall above 2,500 meters [16,17].

The district's population, as per the 2011 census, is approximately 330,000, with rural residents constituting over 90%. Major pilgrimage destinations within or accessed through the district include Gangotri (3,100 m), Yamunotri (3,293 m), Dodital, and Dayara Bugyal. The Gangotri National Park, Govind Pashu Vihar Wildlife Sanctuary, and a portion of the Kedarnath Wildlife Sanctuary fall within the district boundaries, imposing conservation constraints on development activities.  [17]

Figure 1: Map presenting the location and extent of the study area with Elevation in meters

The matrix A satisfies the reciprocal property: aⱼᵢ = 1/aᵢⱼ, and all diagonal elements aᵢᵢ = 1.

4.1.2 Weight Calculation

The priority weights were derived using the eigenvector method. For matrix A, the principal eigenvector w was computed by solving [19]:

A w = λₘₐₓ w

where λₘₐₓ is the largest eigenvalue of A. The normalized eigenvector components represent the relative weights of criteria. Practically, the geometric mean method was employed for computational efficiency:

For each criterion *i*, the geometric mean GMáµ¢ was calculated as:

GMi=j=1naij1n

 

The weight wáµ¢ was then obtained by normalization:

4.1.3 Consistency Ratio (CR)

The validity of pairwise judgments was assessed using the Consistency Ratio. First, the Consistency Index (CI) was computed:

where *n* is the number of criteria. The Consistency Ratio is then:

where RI is the Random Index (average CI of randomly generated matrices). The RI values for different *n* are:

Parameter	Suitable Classes (Score 8-10)	Moderate (Score 5-7)	Unsuitable (Score 1-4)
LULC	Agricultural fallow, barren land, scrubland	Open forest, grassland	Dense forest, snow, water bodies, built-up
Most Visited Places	1-3 km buffer	3-5 km and 0.5-1 km buffer	<0.5 km, >5 km
Distance from Road	500-1,500 m	200-500 m, 1,500-2,500 m	<200 m, >2,500 m
Distance from Town	5-10 km	10-15 km, 2-5 km	<2 km, >15 km
Distance from River	200-500 m	100-200 m, 500-1,000 m	<100 m, >1,000 m
Annual Mean Temp	15-25°C	10-15°C, 25-30°C	<10°C, >30°C
Distance from PA	>2 km	1-2 km	<1 km
Slope	5-15°	15-25°, 2-5°	>25°, <2°
Elevation	1,500-2,500 m	1,000-1,500 m, 2,500-3,000 m	<1,000 m, >3,000 m
Geological Setup	Massive rock (granite, quartzite)	Moderately jointed rock	Fractured/sheared, debris, landslide zone
Earthquake Susceptibility	Low (Zone III)	Moderate (Zone IV)	High-Very High (Zone V)

Note: For Uttarkashi, the entire district is in Zones IV-V, so the EQ parameter effectively acts as a constraint.
PA = Protected Areas

Figure 2: Selected Layers for Assessment and their Classification

Figure 3: Homestay Potential Sites the Study Area Using MCDA Methodology Framework

Figure 4: Google Earth Images Presenting the major Potential Sites in the Region

5.6 Major Findings from Field Verification:

1. High Suitability Verification: Seven of nine high-suitability predicted sites were confirmed as excellent locations. The two discrepancies were: (a) a site near Jhala where the "most visited places" proximity was overestimated because the trekking route had been re-routed; (b) a site near Sukki where local land ownership disputes prevented homestay establishment despite biophysical suitability.
2. Existing Homestay Performance: In high-suitability zones with existing homestays (e.g., Harsil, Bagori, Matli), average occupancy during the tourist season (April-June, September-November) was 68%, with average annual incomes of ₹2.4-3.8 lakhs per homestay. In moderate-suitability zones (e.g., Damta, Naugaon), occupancy averaged 41%. No homestays existed in low/very low predicted zones.
3. Tourist Preferences: Of 68 tourists interviewed at various locations, the following factors were rated most important for homestay selection: cleanliness (98% rated very important), host hospitality (92%), location scenic quality (87%), proximity to trek start point (76%), and road accessibility (71%). These align well with the criteria weights derived from AHP, validating the expert judgment.
4. False Negatives: One site near Sankri (predicted as moderate, field-evaluated as high) demonstrated that the DEM-based slope classification underestimated terrace-able slopes where local farmers have constructed multi-level structures. Similarly, a site in the upper Rupin valley had higher tourist visitation than captured by the Google Earth Pro digitization (which missed a newly popular waterfall trek).
5. Exclusion Zones: Three sites predicted as low were found to be completely unsuitable in the field (false positives) because of: (a) un-mapped seasonal stream that becomes a debris flow channel; (b) proximity to a landslide scar that had reactivated; (c) location within the acoustic disturbance zone of a hydropower project (not captured in any of the 11 parameters). This suggests adding "proximity to hydropower projects" as a future parameter.

Recommendations from Verification: The model demonstrates acceptable accuracy for regional planning (77.1%). However, for site-specific investment decisions, field verification remains essential. The inclusion of earthquake susceptibility as a low-weighted constraint is validated by the field observation that tourists and operators alike prioritize safety, even if not explicitly stated. The verification also confirmed that the 1-3 km buffer from most visited places is optimal; sites within 500 m of Gangotri temple, for example, faced prohibitive land costs (₹25,000-40,000 per sq. m) and regulatory hurdles (failing pollution control board inspections for sewage).

CONCLUSION

This study successfully demonstrated the application of an integrated MCDA-GIS framework for homestay potential site identification in the complex Himalayan terrain of Uttarkashi district, Uttarakhand. The methodology, incorporating eleven physical and locational parameters with AHP-derived weights, produced a spatially explicit suitability map that was validated with 77.1% overall accuracy through extensive field verification.

The key conclusions are:

1. Spatial Patterns: Approximately 12.6% of the district area is highly suitable for homestay development, concentrated along the Bhagirathi river corridor from Harsil to Maneri, with secondary nodes near Sankri and Purola. Moderate suitability covers 23.4%, offering expansion opportunities.
2. Parameter Significance: LULC (23.8%), proximity to most visited places (17.5%), and distance from roads (12.3%) emerged as the three most influential parameters, confirming that tourism demand and accessibility outweigh purely physical factors in homestay viability.
3. Methodological Robustness: The consistency ratio (CR = 0.0216 < 0.10) confirms the reliability of the pairwise comparisons. The weighted overlay approach successfully integrated disparate data types (continuous, categorical, and proximity-based) into a unified suitability index.
4. Policy Implications: The tehsil-level results provide a basis for targeted interventions. Bhatwari tehsil, with the largest high-suitability area (14.2%), should be prioritized for homestay training and subsidy programs. Conversely, Barkot tehsil's low high-suitability percentage suggests alternative tourism models (e.g., day-trip homestays with organic farming experiences) rather than overnight pilgrim accommodation.
5. Limitations and Future Research: The model does not incorporate socio-economic parameters (land ownership patterns, family labor availability, capital access, community willingness) which ultimately determine whether a biophysically suitable site translates into an operational homestay. Additionally, dynamic parameters (climate change impacts on snowfall, evolving tourist preferences post-COVID) require periodic model updating. Future research should integrate machine learning approaches (Random Forest, MaxEnt) to capture non-linear relationships between parameters and actual homestay success rates.

For the district administration, this study provides a scientific basis for: (a) issuing no-objection certificates (NOCs) for homestay registration; (b) prioritizing rural roads and telecom infrastructure investments; (c) designing homestay training curricula tailored to specific zones; and (d) implementing carrying capacity-based caps to prevent over-tourism in high-suitability but ecologically sensitive areas like Harsil and Gangotri.

In conclusion, the MCDA-GIS methodology offers a replicable, transparent, and scientifically defensible framework for homestay planning not only in Uttarkashi but across the Indian Himalayan Region. When combined with community participation and adaptive management, such spatial decision support tools can contribute meaningfully to sustainable mountain tourism and inclusive rural development.

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