Predicting House Demand with Spatial Considerations in a Growing Suburb

By Shahabuddin Amerudin

Introduction

As a real estate developer planning to invest in a growing suburban area, you recognize that housing demand is not solely influenced by time-related factors but also by spatial considerations. To make precise predictions about where and when houses will be in demand, you need to incorporate both temporal and spatial elements into your forecasting.

Defining the Objective

The objective remains to forecast the demand for houses in the suburban area over the next five years, but now with a spatial dimension. You want to estimate the number of new homes that potential buyers are likely to purchase each year while considering the spatial distribution of demand across different neighborhoods within the suburb.

Gathering Data

In addition to the data mentioned earlier, you gather spatial data, including:

  • Geographic information system (GIS) data, which includes information on neighborhood boundaries, zoning regulations, and proximity to amenities.
  • Historical sales data at the neighborhood level, highlighting spatial variations in demand.
  • Spatial economic indicators such as the location of major employers and transportation hubs.

Data Preprocessing

Preprocessing now involves not only cleaning and formatting data but also spatial operations like spatial joins and aggregations. You’ll need to link housing demand data with spatial boundaries to segment demand by neighborhood.

Feature Engineering

For spatiotemporal forecasting, consider features such as:

  • Historical neighborhood-specific housing demand.
  • Spatial variables like distance to schools, parks, and shopping centers.
  • Temporal trends and seasonal patterns.
  • Spatial autocorrelation measures to account for neighborhood interdependencies.

Choosing a Forecasting Method

Given the spatial dimension, your choice of forecasting methods expands:

  1. Spatiotemporal Models: Methods like Spatiotemporal Autoregressive Integrated Moving Average (STARIMA) models can account for both spatial and temporal dependencies.
  2. Spatial Regression: Use spatial regression models like spatial autoregressive models to capture spatial relationships.
  3. Geospatial Machine Learning: Apply geospatial machine learning techniques, including spatially aware algorithms like k-nearest neighbors (KNN) or geospatial neural networks.

Model Training

Train your models while considering both the temporal and spatial aspects. This may involve neighborhood-specific forecasts that are aggregated to provide an overall prediction.

Validation and Evaluation

Evaluation metrics should not only consider forecasting accuracy but also spatial metrics like Moran’s I or Geary’s C to assess the spatial autocorrelation of prediction errors.

Making Predictions

With well-tuned models, predict annual demand for houses in the suburban area while accounting for spatial variations. These predictions provide insights into which neighborhoods are likely to experience increased demand.

Monitoring and Refinement

Continuously monitor demand changes across neighborhoods. Adjust your models as new data becomes available and as the spatial dynamics evolve.

Interpretation and Communication

Analyze the spatial and temporal factors driving house demand within different neighborhoods. Communicate these insights to stakeholders for informed decisions regarding where to invest in new housing developments.

Incorporating spatial elements in your forecasting not only helps you predict overall demand but also allows you to make location-specific decisions, ensuring that your investments are strategically aligned with the spatial dynamics of the growing suburban area.

Interpreting the Results

Understanding the spatial and temporal dynamics of house demand is crucial for your real estate development plans. Here’s how you can interpret and leverage the results:

  • Spatial Clusters: Examine the results for spatial clusters of high demand. Identify neighborhoods where demand is projected to be significantly higher than others. These clusters can guide your investment decisions, directing resources towards areas with strong demand.
  • Spatial Autocorrelation: Assess the spatial autocorrelation of prediction errors. If you find spatial patterns in the errors, it indicates that your model might not be capturing all relevant spatial factors. This insight helps refine your models.
  • Temporal Trends: Analyze the temporal trends in demand within specific neighborhoods. Are certain areas experiencing increasing demand over time? These insights can inform your construction timelines and marketing strategies.
  • Spatial Factors: Investigate which spatial factors contribute most to high demand areas. Factors such as proximity to schools, public transportation, or job centers might play a significant role. Understanding these factors allows you to target specific amenities and services in your developments.
  • Investment Strategy: Armed with spatiotemporal insights, you can create a more targeted investment strategy. Allocate resources to develop housing projects in areas with high predicted demand, while also considering the construction timeline based on temporal trends.
  • Risk Mitigation: Recognize potential risks associated with spatially clustered demand. Overinvesting in a single area can be risky if demand unexpectedly shifts. Diversify your portfolio across neighborhoods to mitigate these risks.

Conclusion

Predicting house demand with spatial considerations in a growing suburb requires a comprehensive approach that combines temporal and spatial forecasting techniques. By incorporating spatial data, understanding neighborhood dynamics, and evaluating spatial autocorrelation, you can make more precise and informed decisions about where and when to invest in housing development projects. This holistic approach to forecasting ensures that your real estate investments are aligned with the spatial realities of a dynamic and growing suburban market, ultimately increasing the likelihood of success in your ventures.

Suggestion for Citation:
Amerudin, S. (2023). Predicting House Demand with Spatial Considerations in a Growing Suburb. [Online] Available at: https://people.utm.my/shahabuddin/?p=6867 (Accessed: 1 September 2023).

Space Demand Analysis for Muslim Cemeteries: Methods, Techniques, and Expectations

Introduction

Space demand analysis is a critical process that helps organizations, developers, and architects to determine the amount of space needed for a particular function or activity. The process involves identifying the space requirements of an organization, project, or event, and then determining the amount of space necessary to meet those requirements. This analysis is important for ensuring that the space is efficient, functional, and cost-effective.

Space demand analysis for cemeteries is a process used to evaluate the amount of land and burial plots needed to accommodate the deceased. It involves identifying the space requirements of a particular cemetery, and then determining the amount of land and burial plots necessary to meet those requirements. The analysis may include factors such as projected population growth, demographic trends, and the cultural and religious customs of the community. The goal of space demand analysis for cemeteries is to ensure that the cemetery has enough land and burial plots to accommodate the deceased for the foreseeable future.

Space demand analysis for Muslim cemeteries in Malaysia is a process used to evaluate the amount of land and burial plots needed to accommodate the deceased according to Islamic customs and laws. It involves identifying the space requirements of a particular Muslim cemetery in Malaysia, and then determining the amount of land and burial plots necessary to meet those requirements. The analysis may include factors such as projected population growth, demographic trends, and the cultural and religious customs of the Muslim community in Malaysia. The goal of space demand analysis for Muslim cemeteries in Malaysia is to ensure that the cemetery has enough land and burial plots to accommodate the deceased for the foreseeable future, while also adhering to the Islamic customs and laws.

The first step in space demand analysis for Muslim cemeteries in Malaysia is to identify the space requirements of the cemetery according to Islamic customs and laws. This includes understanding the demographic trends and projected population growth of the Muslim community in Malaysia. For example, a community with a large aging population may have a higher demand for burial plots in the future. It is also important to consider the cultural and religious customs of the Muslim community in Malaysia. For example, Islamic customs dictate that Muslims should be buried in a specific direction, facing Mecca, and burial must take place as soon as possible after death.

Once the space requirements have been identified, the next step is to determine the amount of land and burial plots necessary to meet those requirements. This includes calculating the number of plots needed for each demographic group and cultural or religious group. For example, a community with a high projected population growth may need more plots than a community with a stable population. It is also important to consider the layout of the cemetery according to Islamic customs and laws, for example, the separation of men and women in the cemetery.

It is also important to consider the cost-effectiveness of the land and plots during the space demand analysis for Muslim cemeteries in Malaysia. This includes understanding the costs associated with the acquisition, maintenance, and operation of the land and plots. For example, a larger piece of land may require more resources to maintain and operate, which could increase costs. Additionally, it is important to consider the long-term costs of the land and plots, such as the costs of expanding or renovating the cemetery in the future.

Finally, it is important to consider the flexibility of the land and plots during the space demand analysis for Muslim cemeteries in Malaysia. This includes understanding how the land and plots can be used for different types of burials in the future. For example, a cemetery that is designed for traditional burials may be difficult to adapt for cremation burials, which are not permissible in Islam, in the future, which could limit the cemetery’s long-term value.

In summary, space demand analysis for Muslim cemeteries in Malaysia is a process that helps cemetery operators to determine the amount of land and burial plots needed for the deceased according to Islamic customs and laws. It involves identifying the space requirements, determining the amount of land and burial plots necessary to meet those requirements, and considering the cost-effectiveness and flexibility of the land and plots while adhering to the Islamic customs and laws. This analysis is important for ensuring that the cemetery has enough land and burial plots to accommodate the deceased for the foreseeable future while also adhering to the Islamic customs and laws.

Techniques and Methods That Can Be Used for Space Demand Analysis

There are several techniques and methods that can be used for space demand analysis, including:

  1. Surveys and questionnaires: Surveys and questionnaires can be used to gather information about the space requirements of the organization, project, or event. This can include information about occupancy levels, functional needs, and spatial layout. Surveys and questionnaires can be distributed to employees, stakeholders, and other users of the space.
  2. Space utilization studies: Space utilization studies involve observing and measuring the actual use of the space. This can include monitoring occupancy levels, tracking the movement of people and materials, and analyzing the flow of work. Space utilization studies can provide valuable information about how the space is actually being used, which can be used to identify areas for improvement.
  3. Space programming: Space programming is a process of identifying the specific requirements of the users of the space. This can include the number of people, the types of activities, and the specific equipment and materials needed. Space programming can help to ensure that the space is designed to meet the needs of the users.
  4. Cost-benefit analysis: Cost-benefit analysis is a method used to evaluate the costs and benefits of different options for the space. This can include evaluating the costs of different types of construction, the costs of different types of equipment, and the costs of different types of maintenance. Cost-benefit analysis can help to ensure that the space is cost-effective.
  5. Geographic Information Systems (GIS) and Spatial Analysis: GIS is a technology that allows to create, manage, analyze and display spatial information, this technology can be used to map and analyze the distribution of population, land use, and other factors that may influence the demand for cemetery space. Spatial analysis helps to identify patterns, trends and relationships in the distribution of the data that can provide valuable insights for the space demand analysis.
  6. Forecasting and projection: Forecasting and projection is a method used to predict future space requirements based on historical data and trends. This can include demographic projections, trends in land use, and other factors that may influence the demand for cemetery space. Forecasting and projection can help to ensure that the cemetery has enough land and burial plots to accommodate the deceased for the foreseeable future.

These are some of the techniques that can be used for space demand analysis. The specific techniques used will depend on the nature of the project and the goals of the analysis.

How GIS Can Help?

Geographic Information Systems (GIS) is a technology that allows the creation, management, analysis, and display of spatial information. In the context of space demand analysis for cemeteries, GIS can be used to identify patterns and trends in the distribution of population, land use, and other factors that may influence the demand for cemetery space.

GIS allows the integration of various types of data, such as demographic data, land use data, and other data relevant to the analysis, into a single system. This data can then be displayed in the form of maps, which can be used to identify patterns and trends in the distribution of population and land use. For example, GIS can be used to create maps that show the distribution of population by age group, gender, and religion. These maps can be used to identify areas of high demand for cemetery space, such as areas with a large aging population.

GIS also allows for the use of spatial analysis techniques, such as spatial statistics, to identify patterns and trends in the data. For example, spatial statistics can be used to identify clusters of high population density, which can indicate areas of high demand for cemetery space. Additionally, GIS can be used to analyze the distance between the population and the existing cemetery, this can help to identify areas where the demand for cemetery space is high but the distance is far, this can indicate the need for a new cemetery.

GIS can also be used to create scenarios and projections of future population growth and land use change. This can help to identify areas where the demand for cemetery space is likely to increase in the future. Additionally, GIS can be used to create a database of all the existing cemetery, this can help to identify the capacity of the existing cemetery, and it also can help to identify where the existing cemetery is located and if it is accessible for the population.

Methodology

Conducting patterns, trends, forecasting, and projection of the space demand for Muslim cemeteries can be done by following these steps:

  1. Collect and organize data: Collect demographic data, land use data, and other relevant data for the area where the Muslim cemetery is located or is planned to be located. This data can include information on population size, population growth, age distribution, gender, religion, and land use. Organize the data in a way that it can be easily analyzed and mapped using GIS.
  2. Use GIS to map and analyze the data: Use GIS to map the data and identify patterns and trends in population and land use. This can include creating maps that show population density, age distribution, and land use. Use spatial analysis techniques, such as spatial statistics, to identify areas of high demand for cemetery space.
  3. Forecasting and projection: Use the historical data and patterns identified in the previous steps to create projections of future population growth and land use change. Use GIS to create scenarios of future population growth and land use change, and identify areas where the demand for cemetery space is likely to increase in the future.
  4. Assess the existing cemetery: Use GIS to create a database of all the existing Muslim cemetery in the area. This can help to identify the capacity of the existing cemetery, and it also can help to identify where the existing cemetery is located and if it is accessible for the population.
  5. Identify the need for new cemetery: Based on the analysis and projections, identify areas where there is a high demand for cemetery space but no existing cemetery or an existing cemetery that is full. This will help to identify the need for a new cemetery.
  6. Plan the new cemetery: Based on the analysis and projections, plan the new cemetery by determining the size, location, and layout of the cemetery according to Islamic customs and laws. This may include determining the direction of the graves and the separation of men and women.
  7. Monitor and evaluate: Continuously monitor and evaluate the demand for cemetery space and make adjustments as necessary. Use GIS to update the data and repeat the analysis as needed.

It’s worth noting that in order to conduct a comprehensive space demand analysis for Muslim cemeteries, it’s important to consider both the demographic and religious requirements, as well as the economic and spatial feasibility. Additionally, it’s important to involve different stakeholders and experts, including community leaders, religious leaders, and cemetery operators, to ensure that the analysis and planning process is inclusive and reflects the needs and preferences of the community.

If using ArcGIS software, space demand analysis for Muslim cemeteries can be done using a combination of GIS tools and techniques. Here is an overview of the process:

  1. Data Preparation: The first step is to prepare the data for analysis. This includes acquiring and formatting demographic data, land use data, cemetery data, transportation data, environmental data, Islamic laws and customs data, and historical data. The data can be imported into ArcGIS and stored in a geodatabase.
  2. Data Analysis: Once the data is prepared, it can be analyzed using various GIS tools and techniques. For example, spatial statistics can be used to identify patterns and trends in population density and land use, kernel density analysis can be used to estimate population density, point pattern analysis can be used to identify patterns in the distribution of the existing cemetery, and time-series analysis can be used to analyze patterns and trends over time.
  3. Scenario Modeling: Scenario modeling can be used to create different scenarios of future population growth and land use change, and to predict the future demand for cemetery space. This can include creating different scenarios of future population growth and land use change and analyzing the impact of each scenario on the demand for cemetery space.
  4. Spatial modeling: GIS models such as Cellular Automata (CA) and Agent-based models (ABM) can be used to simulate and predict future land use change and population growth. These models can help to identify areas where the demand for cemetery space is likely to increase in the future.
  5. Regression analysis: Regression analysis can be used to identify the relationship between the demand for cemetery space and other factors such as population density, age distribution, and land use.
  6. Data visualization: The results of the analysis can be visualized using maps, charts, and graphs in ArcGIS. These visualizations can be used to present the findings and recommendations to stakeholders.
  7. Reports and presentations: The results can be exported to Microsoft Office Suite and presented in a written report or a visual format such as slides.

ArcGIS provides a variety of tools and techniques that can be used to conduct space demand analysis for Muslim cemeteries. The specific tools and techniques used will depend on the nature of the project and the goals of the analysis.

Types of GIS Analysis

There are several types of GIS analysis that can be used for space demand analysis for Muslim cemeteries, including:

  1. Spatial statistics: Spatial statistics can be used to identify patterns and trends in the distribution of population and land use. This can include identifying clusters of high population density, which can indicate areas of high demand for cemetery space. Spatial statistics can also be used to analyze the distance between the population and the existing cemetery, which can help to identify areas where the demand for cemetery space is high but the distance is far.
  2. Network analysis: Network analysis can be used to analyze the accessibility of the existing cemetery to the population. This can include analyzing the distance, travel time, and mode of transportation between the population and the existing cemetery. Network analysis can help to identify areas where the existing cemetery is not easily accessible to the population.
  3. Surface analysis: Surface analysis can be used to analyze terrain and slope of the land. This can include identifying areas that are suitable for cemetery development, such as flat land with good drainage. Surface analysis can also be used to identify areas that are not suitable for cemetery development, such as steep slopes or areas prone to flooding.
  4. Multi-Criteria Decision Analysis (MCDA): MCDA is a method that allows evaluating different alternatives based on multiple criteria, this method can be used to evaluate different options for the location of a new cemetery based on factors such as proximity to the population, accessibility, land use, and terrain.
  5. Scenario modeling: Scenario modeling can be used to create different scenarios of future population growth and land use change, and to predict the future demand for cemetery space. This can include creating different scenarios of future population growth and land use change and analyzing the impact of each scenario on the demand for cemetery space.
  6. Raster and vector data analysis: GIS can handle different types of data such as raster and vector data, Raster data analysis can be used to analyze satellite imagery and aerial photography, this can provide information about the land use and the vegetation cover, which can be useful for identifying suitable areas for cemetery development. Vector data analysis can be used to analyze the data in the form of point, line and polygons, this can provide information about the location of the buildings, roads, and other features that can influence the accessibility of the cemetery.

Scenario modeling is a type of GIS analysis that can be used to create different scenarios of future population growth and land use change, and to predict the future demand for cemetery space. It allows the exploration of the potential outcomes of different decisions or actions, and it is useful for identifying opportunities and risks associated with the different scenarios.

The process of scenario modeling typically involves the following steps:

  1. Identify the key drivers of change: Identify the factors that are likely to influence population growth and land use change in the future, such as economic growth, demographic trends, and government policies.
  2. Create scenarios: Based on the identified key drivers of change, create different scenarios of future population growth and land use change. These scenarios can range from a “business as usual” scenario, in which current trends continue, to a “best-case” scenario, in which the demand for cemetery space is significantly reduced.
  3. Model the scenarios: Use GIS to model the different scenarios of future population growth and land use change. This can include creating maps and other visualizations of the different scenarios, and analyzing the impact of each scenario on the demand for cemetery space.
  4. Evaluate the scenarios: Evaluate the different scenarios by considering factors such as the impact on the demand for cemetery space, the feasibility of each scenario, and the potential benefits and risks associated with each scenario.
  5. Choose a preferred scenario: Based on the evaluation, choose a preferred scenario that best meets the goals of the space demand analysis for Muslim cemeteries. This can include identifying areas where the demand for cemetery space is likely to increase in the future and planning for the new cemetery accordingly.
  6. Monitor and update: Continuously monitor the key drivers of change and update the scenarios as necessary. Repeat the analysis as needed to reflect changes in the population and land use.

Scenario modeling allows for the exploration of different possible outcomes of future population growth and land use change, and it can be used to identify opportunities and risks associated with the different scenarios. Additionally, it can help to ensure that the cemetery has enough land and burial plots to accommodate the deceased for the foreseeable future while also adhering to the Islamic customs and laws.

Using GIS Analysis for Pattern and Trend Analysis

  1. Spatial statistics: Spatial statistics can be used to identify patterns and trends in the distribution of population and land use. For example, using spatial statistics, you can calculate the spatial autocorrelation (Moran’s I, Geary’s C) of the population density to identify if there are clusters of high population density, which can indicate areas of high demand for cemetery space.
  2. Additionally, you can use spatial statistics to identify the Hot-spots (Getis-Ord Gi* statistic) in the population density, this can help to identify areas where the population density is higher or lower than expected.
  3. Kernel density analysis: Kernel density analysis can be used to estimate the density of population in a given area. It creates a continuous surface (raster) that shows the distribution of a point feature such as the population. By analyzing this surface, you can identify areas of high population density, which can indicate areas of high demand for cemetery space.
  4. Point pattern analysis: Point pattern analysis can be used to identify patterns in the distribution of the existing cemetery. For example, you can use point pattern analysis (such as the nearest neighbor index) to identify the distribution of the existing cemetery, this can help to identify areas where there are too many or too few cemetery and the density of the existing cemetery.
  5. Time-series analysis: Time-series analysis can be used to analyze patterns and trends over time. For example, you can use time-series analysis to track the population growth and land use change over time, and identify trends in the demand for cemetery space over time. You can also use this analysis to predict future demand for cemetery space based on historical data.
  6. Regression analysis: Regression analysis can be used to identify the relationship between the demand for cemetery space and other factors such as population density, age distribution, and land use. For example, you can use regression analysis to identify the relationship between population density and the demand for cemetery space, and use the model to predict future demand for cemetery space based on population growth.

These are some examples of how GIS analysis can be used for pattern and trend analysis in the context of space demand analysis for Muslim cemeteries. It’s worth noting that these are not the only analysis that can be used, but these are some common ones that can provide valuable insights. Additionally, these analysis can be combined and integrated to create a comprehensive analysis that considers multiple factors and aspects.

Using GIS Analysis for Forecasting and Prediction Analysis

There are several types of GIS analysis that can be used for forecasting or prediction in the context of space demand analysis for Muslim cemeteries, including:

  1. Scenario modeling: Scenario modeling can be used to create different scenarios of future population growth and land use change, and to predict the future demand for cemetery space. This can include creating different scenarios of future population growth and land use change and analyzing the impact of each scenario on the demand for cemetery space.
  2. Time-series analysis: Time-series analysis can be used to analyze patterns and trends over time, and to predict future demand for cemetery space based on historical data. This can include identifying patterns in population growth and land use change over time, and identifying trends in the demand for cemetery space over time.
  3. Regression analysis: Regression analysis can be used to identify the relationship between the demand for cemetery space and other factors such as population density, age distribution, and land use. This can be used to predict future demand for cemetery space based on population growth and other factors.
  4. Artificial Intelligence and Machine Learning (AI/ML) techniques: AI/ML techniques such as neural networks, decision trees, and random forests can be used to predict future demand for cemetery space based on historical data. These techniques can be trained to identify patterns and relationships in the data, and can be used to make predictions about future demand.
  5. Geographic Information Systems (GIS) models: GIS models such as Cellular Automata (CA) and Agent-based models (ABM) can be used to simulate and predict future land use change and population growth. These models can help to identify areas where the demand for cemetery space is likely to increase in the future.
  6. Statistical models: Statistical models such as the Time-Series Forecast and ARIMA (Auto Regressive Integrated Moving Average) can be used to predict future demand for cemetery space based on historical data. These models can be used to analyze the trend, seasonality, and cyclical behavior of the demand for cemetery space, and make predictions about future demand.
  7. Remote sensing: Remote sensing techniques can be used to predict future land use change and population growth by analyzing satellite imagery and aerial photography. This can include identifying areas that are suitable for cemetery development, such as flat land with good drainage, and areas that are not suitable, such as steep slopes or areas prone to flooding.
  8. Geostatistics: Geostatistics can be used to predict future demand for cemetery space by modeling the spatial dependence of the data. This can include interpolating missing data, predicting future values at unsampled locations, and estimating uncertainty in the predictions.

In summary, there are many types of GIS analysis that can be used for forecasting or prediction in the context of space demand analysis for Muslim cemeteries. These include scenario modeling, time-series analysis, regression analysis, AI/ML techniques, GIS models, statistical models, remote sensing, and geostatistics. The specific type of analysis used will depend on the nature of the project and the goals of the analysis.

Required Data

There are several types of data that are needed for space demand analysis for Muslim cemeteries, including:

  1. Demographic data: This includes data on population size, population growth, age distribution, gender, religion, and other characteristics of the population. This data can be used to identify areas of high demand for cemetery space based on the population’s characteristics and demographics.
  2. Land use data: This includes data on the current land use, such as residential, commercial, industrial, agricultural, and other land uses. This data can be used to identify areas where the demand for cemetery space is high based on the population density, and to identify areas suitable for cemetery development.
  3. Cemetery data: This includes data on the existing cemetery, such as location, size, capacity, and occupancy. This data can be used to identify the capacity of the existing cemetery and to identify areas where there is a high demand for cemetery space but no existing cemetery.
  4. Transportation data: This includes data on the transportation network, such as roads, public transportation, and other modes of transportation. This data can be used to analyze the accessibility of the existing cemetery to the population and to identify areas where the existing cemetery is not easily accessible to the population.
  5. Environmental data: This includes data on the natural environment, such as terrain, slope, vegetation cover, and other environmental factors. This data can be used to identify areas that are suitable or not suitable for cemetery development based on the environmental conditions.
  6. Islamic laws and customs data: This includes data on the Islamic laws and customs related to the cemetery, such as the direction of the graves, the separation of men and women, and other customs. This data can be used to plan the new cemetery according to Islamic customs and laws.
  7. Historical data: This includes data on population, land use, and cemetery data collected over time. This data can be used to analyze patterns and trends over time and to make predictions about future demand for cemetery space.

These are some examples of data that are needed for space demand analysis for Muslim cemeteries. The specific data needed will depend on the nature of the project and the goals of the analysis. It’s important to note that obtaining accurate and up-to-date data is crucial for the reliability and validity of the results.

The length of past data needed to predict future demand for cemetery space for 3-5 years depends on several factors, such as the nature of the population and land use change, the availability of historical data, and the complexity of the forecasting model. In general, a longer time series of historical data will provide more information for the forecasting model to work with and may result in more accurate predictions. However, if the population and land use change have been relatively stable over time, then a shorter time series of data may be sufficient.

In general, a minimum of 3-5 years of historical data is needed to establish a trend or seasonality in the data, this will be used as a base to predict future demand. Additionally, if more data is available it’s always better to have more historical data to feed the forecasting model, as it will provide a better understanding of the underlying patterns and trends in the data.

It’s also important to note that the quality of the data is just as important as the quantity, thus, having accurate and reliable data is crucial for the reliability and validity of the results.

Expected Results from The Analysis

The expected results of space demand analysis for Muslim cemeteries are:

  1. Identification of areas of high demand for cemetery space: The analysis will identify areas where the demand for cemetery space is high based on population density and other factors such as age distribution, land use, and accessibility.
  2. Identification of suitable areas for cemetery development: The analysis will identify areas that are suitable for cemetery development based on factors such as land use, terrain, slope, and accessibility.
  3. Forecasting of future demand for cemetery space: The analysis will predict future demand for cemetery space based on population growth, land use change, and other factors.
  4. Identification of areas with inadequate cemetery accessibility: The analysis will identify areas where the existing cemetery is not easily accessible to the population.
  5. Identification of potential opportunities and risks associated with different scenarios: The analysis will evaluate different options for the location of a new cemetery based on factors such as proximity to the population, accessibility, land use, and terrain and identify the potential opportunities and risks associated with each scenario.
  6. Compliance with Islamic customs and laws: The analysis will ensure that the cemetery has enough land and burial plots to accommodate the deceased for the foreseeable future while also adhering to the Islamic customs and laws.

These results can be presented in a variety of formats, such as:

  1. Maps: The results can be presented as maps that show the population density, land use, accessibility to the existing cemetery, and other factors.
  2. Charts and graphs: The results can be presented as charts and graphs that show population growth, land use change, and other factors over time.
  3. Tables and spreadsheets: The results can be presented as tables and spreadsheets that show population density, land use, accessibility to the existing cemetery, and other factors.
  4. Reports: The results can be presented in a written report that summarizes the findings and recommendations.
  5. Presentations: The results can be presented in a visual format such as slides, that can be used to present the findings and recommendations to stakeholders, such as government officials, community leaders, and cemetery managers.
  6. GIS models: The results can be presented in the form of GIS models, such as Cellular Automata (CA) and Agent-based models (ABM) that simulate and predict future land use change and population growth.
  7. Time-series forecasts: The results can be presented in the form of time-series forecasts, such as statistical models, such as Time-Series Forecast and ARIMA (Auto Regressive Integrated Moving Average) which can be used to predict future demand for cemetery space based on historical data.

The specific format of the results will depend on the nature of the project, the goals of the analysis, and the audience. It’s important that the results are presented in a clear and easily understandable format that highlights the key findings and recommendations.

How to Know the Results Are Good?

There are several ways to determine if a prediction made through space demand analysis for Muslim cemeteries is good:

  1. Comparison with actual data: The prediction can be compared with actual data on population growth, land use change, and demand for cemetery space over time. If the prediction is accurate, it should closely match the actual data.
  2. Evaluation of the model: The prediction can be evaluated by assessing the performance of the model that was used to make the prediction. This can include analyzing the accuracy and precision of the model, as well as its ability to explain the variability in the data.
  3. Sensitivity analysis: A sensitivity analysis can be performed to test how the prediction changes when input parameters are varied. This can help to identify the most important drivers of the prediction and to assess the robustness of the model.
  4. Cross-validation: A cross-validation can be used to assess the prediction by comparing the prediction with a subset of the data that was not used in the model. This can help to ensure that the model has a good generalization performance.
  5. Statistical measures: Several statistical measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared can be used to evaluate the prediction accuracy, these measures are useful for comparing the prediction with the actual data and identify the errors.
  6. Comparison with other predictions: The prediction can be compared with other predictions made by other models or analysts. This can help to identify the strengths and weaknesses of the prediction and to assess its overall accuracy.
  7. Consultation with experts: The prediction can be discussed and evaluated with experts in the field, such as cemetery managers, population experts, and Islamic scholars to assess the validity of the predictions.

It’s important to note that no prediction is 100% accurate, but a good prediction should be based on solid data and a robust model, and should provide insights and recommendations that are consistent with the goals of the analysis and the understanding of the subject matter.

Conclusion

Space demand analysis for Muslim cemeteries is a process of evaluating the current and future demand for cemetery space based on population growth, land use change, and other factors. It is important for identifying areas of high demand for cemetery space, areas suitable for cemetery development, and forecasting future demand for cemetery space. The analysis can be conducted using various GIS tools and techniques such as scenario modeling, time-series analysis, regression analysis, GIS models, artificial intelligence, and machine learning techniques. The specific tools and techniques used will depend on the nature of the project and the goals of the analysis.

Data that is needed for this analysis includes demographic data, land use data, cemetery data, transportation data, environmental data, Islamic laws and customs data, and historical data. The data should be accurate and up-to-date to ensure the reliability and validity of the results.

The results of the analysis can be presented in a variety of formats such as maps, charts, and graphs, tables, and spreadsheets, reports, and presentations. The specific format of the results will depend on the nature of the project, the goals of the analysis, and the audience.

To determine if a prediction is good, several methods can be used such as comparison with actual data, evaluation of the model, sensitivity analysis, cross-validation, statistical measures, comparison with other predictions, and consultation with experts.

In conclusion, space demand analysis for Muslim cemeteries is a valuable tool for identifying areas of high demand for cemetery space, areas suitable for cemetery development, and forecasting future demand for cemetery space. The analysis should be conducted using accurate and up-to-date data, and the results should be presented in a clear and easily understandable format that highlights the key findings and recommendations.

 

Trends of the future: 2025 and beyond

  • 10% of people will be wearing clothes connected to the Internet
  • 80% will have unlimited (sponsored) backup space in the cloud
  • There will be one trillion sensors connected to the internet
  • 80% of the world’s population will have Internet presence
  • The first automobile entirely produced with a 3D printer
  • 90% of world’s population will own a smartphone
  • 90% of world’s population will have internet access
  • 10% of all vehicles on the roads will be driverless
  • More than 50% of home appliances will be connected to Internet
  • More rides will be made on shared cars than on private cars

Source: World Economic Forum 2015

Top five trends in GIS technology

According to Dangermond, the top five trends in GIS technology today are as follows:

  1. Location as a service
  2. Advanced analytics
  3. Big data analytics
  4. Real-time GIS
  5. Mobility

Dangermond continues: “The last leap in computing was the shift from the server to the cloud. Software as a service (SaaS) opened a world of opportunities for GIS, as shared map services like the World Imagery basemap are no longer separate from the unique services offered to users. GIS users can share data, collaborate, make mashup maps in the server, and then connect to the cloud.

The next leap in GIS technology and computing is connecting to the vast network of devices providing data in real time. This technology is a revolutionary change and brings great opportunity. The more accessible data is, the more important it will be to understand it. And maps are the visual language for understanding the context of data.”

Excerpt From: Amor Laaribi. “GIS and the 2020 Census.” iBooks.