Quantitative estimates increase the perceived credibility, hence are preferred for decision-making purposes. Spatial models also take quantitative estimates as input to generate scenario map.

Quantitative estimates of  scenario settings and scenario targets, but not limited to: total demand of land use types, transition matrix, relations between explanatory spatial factors and land use transition, priorities of transitions and factors, etc. 

Depend on data availability and the level of stakeholder engagement, quantitative estimates can be derived from empirical data, elicited from expert judgement, or assigned by stakeholder's vision.

Trend-based scenarios tools can extract demand estimates from historical data with embedded functions, and project the relations to future. The output is usually taken as the baseline scenario.  

To create alternative future scenarios, one may model the demand with optional situations of driving force variables, model the interaction between agents with different preference settings, or retrieve the estimates from expert knowledge or stakeholder vision.

 

Related approaches and tools:

Global socio-economic scenario estimates by nations and regions: International Futures

To decide estimates based on past trend: past change ratio, Markov Chain (provided with most structured modeling tools)

To derive estimates from scenario value of explanatory factors: system dynamics, regression, economic models, urban growth model, decision tree

To quantify expert judgement or stakeholder vision: Bayesian belief network, surveys, interviews 

To decide the relative importance and priority of the driving forces: Saaty 9 point scaling method

 

Readings:

Voinov, A., & Bousquet, F. (2010). Modelling with stakeholders. Environmental Modelling & Software, 25(11), 1268-1281.

Kelly, R. A., et al (2013). Selecting among five common modelling approaches for integrated environmental assessment and management. Environmental Modelling & Software, 47, 159-181.

Genre, V., Kenny, G., Meyler, A., & Timmermann, A. (2013). Combining expert forecasts: Can anything beat the simple average?. International Journal of Forecasting, 29(1), 108-121.