Realistic Land Use Research Articles

Does partition matter? A new approach to modeling land use change

Cellular automata (CA) is a classical method for studying land use change. However, homogeneous transformation rules have commonly been used to conduct simulations in the past, and these rules seldom consider the spatial heterogeneity of geographic elements. Therefore, in this study, we incorporated spatial-temporal heterogeneity transformation rules into the CA framework based on spatial data mining. A model that couples self-organizing maps (SOM), hierarchical clustering (HC), and patch generation land use simulation (PLUS) was proposed; it is called the SOM-HC-PLUS model. This model considers the difference in local-area driving factors, and therefor it not only determines the optimal partition scheme automatically, but it also measures the contribution of partition driving factors. The Jinpu New Area in Dalian, China, was used to test the validity of the model by comparing the traditional PLUS model and the administrative division (AD)-PLUS model based on AD partition. The results showed that the partition scheme of the SOM-HC-PLUS model was reasonable and credible. Further, compared with other models, this model showed higher simulation accuracy and a more realistic land use distribution pattern. The driving factors showed significant differences in the overall and regional intensity. Moreover, the importance of natural environmental conditions, represented by elevation factors, in the expansion of artificial surfaces increased significantly. By 2030, artificial surfaces were projected to increase significantly through the conversion of cultivated lands. The sustainable development scenario showed a more compact patch layout and exhibited better protection of grasslands and forests than the historical development scenario. In summary, this study proposed a mixture CA model based on the idea of geographic partition, one that proved the reliability of the SOM-HC-PLUS model to conduct spatial-temporal heterogeneity studies on land use partition. It provides the possibility to explore patterns of regional land use changes over multiple periods, and can assist in urban planning and management and promoting sustainable development.

Impact of different land use data on WRF model short range forecasts during pre-monsoon and monsoon seasons in India

This study focuses to analyse the impact of land use changes on short range weather forecasts over Indian region. Weather Research and Forecasting (WRF) model simulation experiments are conducted by using land use data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Indian Space Research Organization (ISRO) satellites for pre-monsoon and monsoon season. MODIS 2001 land use is used in control (CNT) experiment and updated land use with recent urban class from MODIS (EXP1) and ISRO (EXP2) for the year 2019 is used to generate model lower boundary conditions in other two experiments. Quantitative error measures and skill score computed for rainfall forecast showed that model skill is better with the use of realistic recent land use data from MODIS and ISRO during pre-monsoon and monsoon period. Extreme Dependency Index score computed also revealed that model skill in predicting extreme rare rainfall events is improved with recent landuse data. Model simulated surface meteorological variables and profiles at lower levels also found to be improved with the inclusion of realistic land use class from MODIS and ISRO. Between the two experiments, the one which used ISRO based land use showed larger improvement particularly during the monsoon season.

Simulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata: a case study on city of Toronto

ABSTRACT The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes. Conventional studies on land use simulation usually simplified the accessibility as proximities and adopted the grid-based simulation strategy, leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types. To overcome these limitations, an Accessibility-interacted Vector-based Cellular Automata (A-VCA) model was proposed for the better simulation of realistic land use change among different urban functional types. The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process. The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto, Canada, during 2012–2016. A vector-based CA without considering the driving factor of accessibility (VCA) and a popular grid-based CA model (Future Land Use Simulation, FLUS) were also implemented for comparisons. The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature (kappa = 0.907, figure of merit = 0.283, and cumulative producer’s accuracy = 72.83% ± 1.535%). The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models, suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy. The proposed model provides new tools for a better simulation of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.

Simulating sediment discharge at water treatment plants under different land use scenarios using cascade modelling with an expert-based erosion-runoff model and a deep neural network

Abstract. Excessive sediment discharge in karstic regions can be highly disruptive to water treatment plants. It is essential for catchment stakeholders and drinking water suppliers to limit the impact of high sediment loads on potable water supply, but their strategic choices must be based on simulations integrating surface and groundwater transfers and taking into account possible changes in land use. Karstic environments are particularly challenging as they face a lack of accurate physical descriptions for the modelling process, and they can be particularly complex to predict due to the non-linearity of the processes generating sediment discharge. The aim of the study was to assess the sediment discharge variability at a water treatment plant according to multiple realistic land use scenarios. To reach that goal, we developed a new cascade modelling approach with an erosion-runoff geographic information system (GIS) model (WaterSed) and a deep neural network. The model was used in the Radicatel hydrogeological catchment (106 km2 in Normandy, France), where karstic spring water is extracted to a water treatment plant. The sediment discharge was simulated for five design storms under current land use and compared to four land use scenarios (baseline, ploughing up of grassland, eco-engineering, best farming practices, and coupling of eco-engineering/best farming practices). Daily rainfall time series and WaterSed modelling outputs extracted at connected sinkholes (positive dye tracing) were used as input data for the deep neural network model. The model structure was found by a classical trial-and-error procedure, and the model was trained on 2 significant hydrologic years. Evaluation on a test set showed a good performance of the model (NSE = 0.82), and the application of a monthly backward-chaining nested cross-validation revealed that the model is able to generalize on new datasets. Simulations made for the four land use scenarios suggested that ploughing up 33 % of grasslands would increase sediment discharge at the water treatment plant by 5 % on average. By contrast, eco-engineering and best farming practices will significantly reduce sediment discharge at the water treatment plant (respectively in the ranges of 10 %–44 % and 24 %–61 %). The coupling of these two strategies is the most efficient since it affects the hydro-sedimentary production and transfer processes (decreasing sediment discharge from 40 % to 80 %). The cascade modelling approach developed in this study offers interesting opportunities for sediment discharge prediction at karstic springs or water treatment plants under multiple land use scenarios. It also provides robust decision-making tools for land use planning and drinking water suppliers.

Impacts of urbanization on atmospheric circulation and aerosol transport in a coastal environment simulated by the WRF-Chem coupled with urban canopy model

This study examines the impacts of urbanization on the local atmospheric circulation and the dispersion of air pollutants over the populated city of Ulsan, South Korea, located in the coastal region. Two experiments are conducted using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the urban canopy model (UCM) at 1-km horizontal resolution. The model experiments are conducted in August for three consecutive years of 2016–2018, with an updated land use category and diurnally-varying surface emission and anthropogenic heat flux. The impacts of urbanization are isolated by comparing the control run (CTRL) with realistic land use conditions and the sensitivity run (NOURB) that replaces the urban surfaces with non-urban grasslands. CTRL reproduces the urban heat island (UHI) and associated wind patterns realistically with reliable local land-sea breeze circulations. The positive temperature anomalies develop over the urban area due to enhanced surface heat flux at the urban surfaces, driving low-level convergence and secondary circulation. Enhanced heating by UHI changes the ground-level aerosol concentration differently. While the concentration does not change significantly by UHI in the daytime, it is reduced considerably at night in the urban areas due to enhanced vertical mixing. The dominant process that the urbanization modifies the aerosol concentration is the thermodynamical effects, which are also supported by the observation.

Quantitative identification and spatial analysis of land use ecological-production-living functions in rural areas on China's southeast coast

The quantitative identification of land use functions (LUFs) forms the basis of land use planning and management. Based on the widely recognized “ecological-production-living” function in sustainable development, a uniform classification and value evaluation system of LUFs for China's rural land use planning and management is established. To highlight the functions of ecological regulation, product supply and living security, the LUFs were divided into 8 primary functions and 20 subfunctions. LUF value evaluation function groups were established based on the indirect or direct value of land use. For empirical research, Fengzhou Town, a typical rural coastal area in southeastern China, was selected as an example. The results show that the values of the ecological, production and living functions account for 14.31%, 44.54% and 41.15% of the total value, respectively. This finding indicates that the primary direction of land use in the study area is oriented toward pursuing the production and living functions. The spatial distribution of the values of the production and living functions shows obvious consistency, and they are also clearly complementary with the ecological function. More than 90% of the land area is assigned double or triple functions, which indicates that the multifunctional characteristics of land use are significant, while the spatial function zoning is disordered. These findings are consistent with the socioeconomic development of the study area, demonstrating that the established classification and value evaluation system of LUFs can reliably reflect realistic land use and will provide scientific support for the multifunctional utilization and effective management of rural land in China.

Managing forests for global and local ecosystem services: A case study of carbon, water and livelihoods from eastern Indonesia

Despite a recent increase of interest in global payment for ecosystem services (PES) mechanisms, there has been little comprehensive assessment of PES impacts on ecosystem services (ESs) at smaller scales. Better understanding of localized impacts of global PES can help balance ES deliveries for global benefits with those for meeting landscape and local level needs. Using a case study from eastern Indonesia, we assessed trade-offs and potential synergies between global PES (e.g. REDD+ for forest carbon) and landscape level ESs (e.g., water quantity, quality, regulation) and local ESs (e.g. forest products for food, energy, livelihoods). Realistic land use change scenarios and potential carbon credits were estimated based on historical land use changes and in-depth interviews with stakeholders. We applied a process-based hydrologic model to estimate changes in watershed services due to land use changes. Finally, local community’s forest uses were surveyed to understand locally realized ESs. The results show empirical evidence that, without careful consideration of local impacts, a PES mechanism to protect global ESs can have negative consequences for local ecosystem services. We present management alternatives designed to maximize positive synergies between different ESs at varying scales.

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation: A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level (surface-sensitive) channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets. Here, we used an improved land use and leaf area index (LAI) dataset in the WRF-3DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels (e.g., channel 3), the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

We developed a geographic cellular automata (CA) model based on partial least squares (PLS) regression (termed PLS-CA) to simulate dynamic urban growth in a geographical information systems (GIS) environment. The PLS method extends multiple linear regression models that are used to define the unique factors driving urban growth by eliminating multicollinearity among the candidate drivers. The key factors (the spatial variables) extracted are uncorrelated, resulting in effective transition rules for urban growth modeling. The PLS-CA model was applied to simulate the rapid urban growth of Songjiang District, an outer suburb in the Shanghai Municipality of China from 1992 to 2008. Among the three components acquired by PLS, the first two explained more than 95% of the total variance. The results showed that the PLS-CA simulated pattern of urban growth matched the observed pattern with an overall accuracy of 85.8%, as compared with 83.5% of a logistic-regression-based CA model for the same area. The PLS-CA model is readily applicable to simulations of urban growth in other rapidly urbanizing areas to generate realistic land use patterns and project future scenarios.

Urban food crop production capacity and competition with the urban forest

The sourcing of food plays a significant role in assessing the sustainability of a city, but it is unclear how much food a city can produce within its city limits. In this study, we propose a method for estimating the maximum food crop production capacity of a city and demonstrate the method in Seattle, WA USA by taking into account land use, the light environment, and a mix of food crops necessary to supply a year-round vegetarian diet. By artificially removing trees from the city, we estimate the effect of tree shading on food crop production capacity. We find that at maximum food production, urban food crops can produce between 1% and 4% of the city's food needs under the most realistic land use scenarios, and that tree shading reduces food crop production capacity between 19% and 35%. We expand beyond the city Seattle limits to find that a buffer of 58km around the city is required to meet 100% of the city's food needs.

Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data

This study addressed the classification of multi-temporal satellite data from RapidEye by considering different classifier algorithms and decision fusion. Four non-parametric classifier algorithms, decision tree (DT), random forest (RF), support vector machine (SVM), and multilayer perceptron (MLP), were applied to map crop types in various irrigated landscapes in Central Asia. A novel decision fusion strategy to combine the outputs of the classifiers was proposed. This approach is based on randomly selecting subsets of the input dataset and aggregating the probabilistic outputs of the base classifiers with another meta-classifier. During the decision fusion, the reliability of each base classifier algorithm was considered to exclude less reliable inputs at the class-basis. The spatial and temporal transferability of the classifiers was evaluated using data sets from four different agricultural landscapes with different spatial extents and from different years. A detailed accuracy assessment showed that none of the stand-alone classifiers was the single best performing. Despite the very good performance of the base classifiers, there was still up to 50% disagreement in the maps produced by the two single best classifiers, RF and SVM. The proposed fusion strategy, however, increased overall accuracies up to 6%. In addition, it was less sensitive to reduced training set sizes and produced more realistic land use maps with less speckle. The proposed fusion approach was better transferable to data sets from other years, i.e. resulted in higher accuracies for the investigated classes. The fusion approach is computationally efficient and appears well suited for mapping diverse crop categories based on sensors with a similar high repetition rate and spatial resolution like RapidEye, for instance the upcoming Sentinel-2 mission.

Carbon, nitrogen, and water response to climate and land use changes in Pennsylvania during the 20th and 21st centuries

Future climate projections indicate that Pennsylvania will get significantly warmer and wetter due to continued increases in atmospheric greenhouse gas (GHG) concentrations. Using the Terrestrial Ecosystem Model version Hydro2 (TEM-Hydro2), this study explores the effect of different climate and land use scenarios on carbon, nitrogen, and water dynamics during the 20th and 21st centuries. TEM-Hydro2 runs are forced by historical 20th century climate data and by 21st century climate projections from the NCAR CCSM3.0 IPCC A2 and B1 scenarios, a relatively high and low GHG emissions scenario, respectively. Regrowing forests are the only ecosystem with positive Net Carbon Exchange (NCE) and sequestered more than 12,000gCm−2 during the 20th century. The highest rates of leaching of dissolved inorganic nitrogen (DIN) occurred in fertilized croplands in the 20th century. Twenty first century runoff increases by 30% in the A2 scenario and 20% in the B1 scenario, but DIN leaching only increases in the A2 scenario. DIN leaching depends upon both runoff and available inorganic nitrogen, which decreases due to high productivity and enhanced plant nitrogen uptake. The effect of increasing urbanization in the 21st century is to reduce NCE by about 34% in both climate scenarios, while water runoff increases by 5% and DIN leaching decreases by 17%. The reduced leaching is the result of converting agricultural land to suburban areas, which are a combination of turflawn and forests, both of which have lower leaching rates than croplands or pastures. Incorporating realistic forest stand age substantially increases the NCE but has little effect on runoff or DIN leaching. Maize yields decrease in the A2 scenario due to the excessive leaching, but increase in the B1 scenario. These results illustrate why it is important to include scenarios of both GHG emissions and realistic land use changes in model projections of the regional impacts of climate change in the 21st century.

Impacts of near-future cultivation of biofuel feedstocks on atmospheric composition and local air quality

Abstract. Large-scale production of feedstock crops for biofuels will lead to land use changes. We quantify the effects of realistic land use change scenarios for biofuel feedstock production on isoprene emissions and hence atmospheric composition and chemistry using the HadGEM2 model. Two feedstocks are considered: oil palm for biodiesel in the tropics and short rotation coppice (SRC) in the mid-latitudes. In total, 69 Mha of oil palm and 9 Mha of SRC are planted, each sufficient to replace just over 1% of projected global fossil fuel demand in 2020. Both planting scenarios result in increases in total global annual isoprene emissions of about 1%. In each case, changes in surface concentrations of ozone and biogenic secondary organic aerosol (bSOA) are substantial at the regional scale, with implications for air quality standards. However, the changes in tropospheric burden of ozone and the OH radical, and hence effects on global climate, are negligible. Over SE Asia, one region of oil palm planting, increases in annual mean surface ozone and bSOA concentrations reach over 3 ppbv (+11%) and 0.4 μg m−3 (+10%) respectively for parts of Borneo, with monthly mean increases of up to 6.5 ppbv (+25%) and 0.5 μg m−3 (+12%). Under the SRC scenario, Europe experiences monthly mean changes of over 0.6 ppbv (+1%) and 0.1 μg m−3 (+5%) in June and July, with peak increases of over 2 ppbv (+3%) and 0.5 μg m−3 (+8 %). That appreciable regional atmospheric impacts result from low level planting scenarios demonstrates the need to include changes in emissions of reactive trace gases such as isoprene in life cycle assessments performed on potential biofuel feedstocks.

Tackling landslide risk: Helping land use policy to reflect unplanned housing realities in the Eastern Caribbean

Landslide risk reduction in developing countries is preferable to disaster remediation and recovery, in both social and economic terms. The implementation of landslide preventative measures is closely related to the development of ‘good practice’ in land use management, at government and community levels, especially in the face of rapid and unplanned urbanisation. These areas of poverty and vulnerability to ‘natural’ hazards present a challenging environment for which to develop realistic land use policies and practices. There is little evidence of on-the-ground delivery of landslide risk reduction. However, scientifically based landslide risk reduction strategies can be effective. In the Eastern Caribbean construction of drainage networks in communities afforded an improvement in slope stability – after a 1 in 100-year rainfall event there were no landslides on previously unstable slopes in densely populated urban communities. This has been recognised in policy terms in the first ever Caribbean-wide, 5-year risk reduction programme. Such evidence represents an important first step in developing realistic land use policies for landslide-prone areas occupied by those migrating to urban centres in the Eastern Caribbean.

The Visioning Approach toward the Integration of Land Use and Transportation

The visioning process, which is newly introduced in the regional planning process, has affected the existing growth planning process. It directly influenced the way of modeling the small area growth, while it has no or little effect on the way of forecasting the large area growth. The scenario approach was actively used as a significant way to identify alternative growth patterns. Four major findings of the growth planning process from the recent visioning approach in Southern California are as follows: First, growth planning is deemed as a participatory and interactive process in a collaborative framework. Second, growth planning relies on a very small geographical scale for better interactive communication. Third, people-driven growth planning puts more emphasis on the qualitative and bottom-up approach than on the quantitative and top-down approach in allocating regional growth into a grid cell. Fourth and last, the performance measures of transportation and environment are used to evaluate strengths and weaknesses of the growth planning scenarios. The interactive evaluation process of diverse scenarios results in selecting or searching the most desirable and realistic land use and transportation scenario.

Impact of land use on Costa Rican tropical montane cloud forests: Sensitivity of orographic cloud formation to deforestation in the plains

The current study provides new insights into the coupling of land use in lowland and premontane regions (i.e., regions below 1000 m) and orographic cloud formation over the Monteverde cloud forests. Rawinsondes launched during the Land Use Cloud Interaction Experiment (LUCIE) together with those from the National Centers for Environmental Prediction (NCEP) provided profiles that were used to drive the Colorado State University Regional Atmospheric Modeling System (CSU RAMS) model, which simulated three realistic land use scenarios (pristine forests, current conditions and future deforestation). For current conditions, the model‐simulated clouds were compared against those observed at hourly intervals by the Geostationary Environmental Observational Satellite–East (GOES E) satellite. The model performed best on 6 different days. The model‐simulated profiles of dew point and air temperatures were compared with the observed profiles from rawinsondes for these days. There was generally very good agreement below 700 mb, the region of the atmosphere most crucial to the cloud forests. The average model simulations for the 6 days show that when the lowland and premontane regions were completely forested, the orographic cloud bank intersected the mountains at the lowest elevations, covered the largest land surface area and remained longest on the surface in the montane regions. Deforestation has decreased the cloud forest area covered with fog in the montane regions by around 5–13% and raised the orographic cloud bases by about 25–75 m in the afternoon. The model results show that further deforestation in the lowland and premontane regions would lead to around 15% decrease in the cloud forest area covered with fog and also raise the orographic cloud base heights by up to 125 m in the afternoon. The simulations show that deforestation in the lowland and premontane regions raises surface sensible heat fluxes and decreases latent heat fluxes. This warms the air temperature and results in a lower dew point temperature of air masses that blow over the lowland and premontane regions. These air masses when lifted form the orographic cloud bank at higher elevations.

Institutional Control: Impacts on Human Health Risk Assessment Exposure Scenario Development

Abstract The concept of institutional control has been present in the environmental industry since the advent of the Comprehensive Environmental Response, Compensation and Liability Act of 1976. Many federal facilities undergoing environmental remediation under this Act have made valuable use of institutional control as an interim remedial action and as an important portion of final remedial alternatives. The core concept of institutional control has been to put in place administrative policies or physical barriers between potentially exposed populations and a contaminant source to avoid unwanted exposures to the potentially exposed populations. The concept of estimating the risk to humans from exposure to contamination and its use as a defining criteria as to whether a site must undergo active remediation have been around just as long. Institutional control and human health risk assessment are intimately linked as crucial parts of the greater remedial alternative analysis topic—human health risk assessment being a determining criteria for proceeding with active remediation and institutional control being one of those remedial alternatives. Institutional control’s core concept has very dramatic influences on the science and practice of human health risk assessment. A vital portion of human health risk assessment involves defining probable human receptor populations by using a set of criteria of which land use scenarios are a key part. A foundational goal of human health risk assessment has been to define realistic land use scenarios that can limit the number and types of potential exposed populations for which risk estimates must be calculated. Institutional control by its regulatory definition limits potential land use on a given contaminated site, therefore limiting the realm of potential (realistic) exposed populations that should be considered in a human health risk assessment. This relationship makes the early identification of institutional control as part of an overall final remedy paramount to conducting efficient realistic human health risk assessment. In successfully doing so, remedial action managers and risk assessors must confide closely in each other to determine the probability of institutional control as a viable remedial action. Such cooperative strategy saves resources for both the remedial action manager and the risk assessor by avoiding unnecessary work to account for improbable exposure scenarios that can drive unnecessary remediation. The early consideration of institutional controls in defining future land use scenarios and therefore risk assessment exposure scenarios saves considerable money, time, and resources involved in successful remediation projects.

Exploring land use scenarios, an alternative approach based on actual land use

Land use scenarios should be able to describe land use as a result of changing biophysical and socioeconomic conditions, as well as the pathways of possible future developments including feedbacks between land use and its drivers. Several approaches exist, to date, to develop regional and national scenarios: (a) explorative biophysical studies which explore the biophysical boundaries of the ‘solution space’; (b) socioeconomic explorative studies which couple calculated biophysical potentials with crude socioeconomic estimates. An alternative approach, based on actual and past land use and its biophysical and demographic drivers as integrated within the multi-scale land use change model CLUE, is presented and discussed. In combination with existing explorative approaches, this approach may contribute to more realistic land use projection scenarios.

Development of an Environmental Geology Data Base for Land Use Planning

A study involving a computerized data base for land use planning at the county level required early decisions (1) in the selection of physical characteristics that best describe the land relative to planning and (2) the size of the data cell for encoding and storing this information. These aspects of the complete study, which are necessary features for all computer-based land use plans, are explored in this paper. Information related to areal distribution and engineering characteristics of earth materials, topography, surface and ground water aspects, provides the needed input data for the base. Such data when supplemented with economic, cultural, and demographic information provide the basis on which planning agencies can formulate realistic land use plans. This information is digitized for computer storage and subsequent computation to be used for decision making. The first step in determining the suitability of a piece of land involved selection of the variables, which are the physical characteristics of the land. In addition, the relative importance of each variable, in relation to a given land use, is evaluated. Next, all the subcategories of each of the variables are assigned levels of importance, called ranking. These procedures are then followed by computer processing whereby the product of level-ranking and variable weighting is computed for each data point for a specific land use. The computer then sums up these products for all the variables and assigns a numerical ranking for each data point. Maps are subsequently printed out depicting the numerical value for each point from which locations with the greatest potential for the specific land use, corresponding to the highest number, can be determined. Selection of cell size for encoding data is a primary task in data base studies for land use planning. A cell provides the dimension of the land surface on which the researcher must determine the nature of the material and supply the input for the data base. The time needed to encode data increases rapidly with decreasing cell size for the same total area and is accompanied by an attendant increase in cost. Based on statistical analysis a cell measuring 154.2 m (500 ft) on the side was found to be most suitable for land use planning at county level.