Principles of Urban Design

1.       Design for All

Urban design should involve people, local communities and those likely to move in

2.       Create places for people

For places to be well-used and well-loved, they must be safe, comfortable, varied and attractive. Vibrant places offers opportunities for meeting people.

3.       Conserve heritage

New developments should conserve monuments, groups of buildings, or sites of cultural importance and natural features.

4.       Enrich the existing

New developments should enrich and complement existing places.

5.       Make connections

Places need to be accessible and integrated with their surroundings.

6.       Work with nature

Places must balance the nature and the man-made environment

7.       Mix uses and forms

Stimulating enjoyable and convenient places

8.       Manage the investment

For projects to be well cared of, they must be economically viable, well managed and maintained

9.       Design for change

New developments needs to be flexible enough to respond to future changes in use.

Reference: Site and Area Planning Development(ppt.) prepared by Arch/EnP. Kurt A. Capate

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Green Growth

The following is the step by step process by which we can integrate green growth in the development plan, as follows:

Step 1 – Getting Organized

Consider Biodiversity Integration Activities Resource Inventory/Mapping Management Planning

Step 2 – Identifying Stakeholder

Include PAMB and other local Stakeholders; LEIPO, DTI, Business Membership Org./Industry Asso.

Step 3 – Setting the Vision

Consider as one of the Vision Elements and Descriptors

Step 4 – Analyzing the Situation

What are the issues and concerns that needs to be addressed (to be incorporated in analysis matrix)

Know your Protected Areas and Critical Habitats/biodiversity; summary report/profile of key sectoral/thematic areas :
- socio-economic
-demographic
-income
-production
-land use resource conditions
-ecological/environmental
-settlements

Step 5 – Setting the Goals and Objectives

Health and safe communities

Food Security

Well manage and sustainable use of land and its resources and access to all infrastructure services

A convenient, equitable, healthful, attractive environment and disaster resilient communities

Good governance

Step 6 – Establishing Development Thrust and Spatial Strategies

Siting/locating agriculture and forest production areas; the development of infrastructure, settlements and other uses like industries, commercial areas are important for sustainable environmental growth and development. Sectoral development activities should be aligned with biodiversity conservation concerns and other spatial strategies. 

Two concerns under CLUP Step 6 :

Sectoral economic development thrusts, and Spatial development strategies as basis for land use planning

Structural map from this step contains the general location of development areas, conservation areas influencing the proposed major infrastructure projects as indicated by the:  direction of urban expansion  proposed circulation system and growth areas/nodes

Step 7 - Preparing the Land Use Plan:  Determining the location and extent of PA/KBA/CH,  MPA and critical watersheds; the economic development

The land use plan is the major interpretation of the agreed vision, goals and objectives, development thrust, and spatial strategies. 

The proposed land use map should contain the location and extent of the delineated PA/KBA and critical watersheds and MPAs.

- a map reflects the location/extent of the economic development/s
- Green growth strategies and policies
- List of green growth programs and projects towards sustainable economic growth

Source:
Estur, Zenaida, Presentation & Discussion on Basic Concepts & Principles-Orientation on the Enhanced CLUP Guidebooks ppt., 2016

Demographic Study

Relevance of Demographic Studies in Land Use Planning

       The center of all our planning effort is the people. We are much concerned about their various current and future needs. These growing needs for goods and services spell the need to determine specific and sufficient spaces for land and their efficient uses for the people to experience quality of life. One must also understand that it is the people that cause environmental and land use issues and problems. It is also the people that have the capacity to manage the resources.

       To ensure that this demand for land will be efficiently addressed, it is important that demographics should be carefully studied, specifically, the current figures, the characteristics or attributes of the population of a particular area, its growth and movement or trend, as these shape land uses encompassing years and decades.

       To come up with various alternatives and decisions in land use planning, knowledge about the demographics by the planners and the community will be most useful.

Population Projection

       Population projection is necessary to determine the trend in increase of population in the coming years, say five or ten years or more. This is important so as to determine the corresponding increase in demand for economic goods, social services, infrastructures support and land for various uses. With these calculations, planners and the decision-makers will be able to make some assumptions as to what will be the issues and concerns that would crop up in the future and as early as today prevent such future problems. These will also help them assess as to how much resources or fund should be raised to efficiently address such future needs. In other words, population projection helps planners and the decision makers come up with a solution before the arrival of a problem.

Two methods for Projecting Future Population

          There are different methods of projecting future population. However, the HLURB CLUP Guide suggests the combined use of Geometric and Participation Rate methods as discussed below:

a. Geometric Method is a computation of three factors including the base population, time interval and population after the given time interval using the formula, Pn = Po (1+r)t where Po = base population of an area, Pn = population of the area t years later, t = time interval in calendar year between Po and Pn , and r = growth rate of population per unit time. To compute growth rate, the formula can be rewritten into, r =( ln [Po / Pn]) / t.

b. Participation Rate Method uses ratio or percent share of a population segment of population to total population as indicated on the sample formula, Participation Rate of working group = Population of 15-64 y. o. / Total Population

Source: CLUP Guidebook Volume 2, 2014, p.183

Different Ways to Visualize Spatial Data

Choosing the right type of visualisation depends on one needs to show (comparison, distribution, composition, or relationship), how much detail the audience needs, and what information the audience needs in order to be successful.

Although the amount of data visualisation options may feel overwhelming, whichever one will choose will be much more comprehensible than raw numbers alone.

2D Area

2D area types of data visualisation are usually geospatial, meaning that they relate to the relative position of things on the earth’s surface.

1. Cartogram: A cartogram distorts the geometry or space of a map to convey the information of an alternative variable, such as population or travel time. The two main types are area and distance cartograms.

2. Choropleth: A choropleth is a map with areas patterned or shaded to represent the measurement of a statistical variable, such as most visited website per country or population density by state.

3.  Dot Distribution Map: A dot distribution or dot density map uses a dot symbol to show the presence of a feature on a map, relying on visual scatter to show spatial pattern.

Temporal

Temporal visualisations are similar to one-dimensional linear visualisations, but differ because they have a start and finish time and items that may overlap each other.

4. Connected Scatter Plot: A connected scatter plot is a scatter plot, a plot that displays values of two variables for a set of data, with an added line that connects the data series.

5. Polar Area Diagram: A polar area diagram is similar to a traditional pie chart, but sectors differ in how far they extend from the center of the circle rather than by the size of their angles.

6. Time Series: A time series is a sequence of data points typically consisting of successive measurements made over a time interval, such as the number of website visits over a period of several months.

Multidimensional

Multidimensional data elements are those with two or more dimensions. This category is home to many of the most common types of data visualisation.

7. Pie Chart: A pie or circle chart is divided into sectors to illustrate numerical proportion; the arc length and angle of each sector is proportional to the quantity it represents.

8. Histogram: A histogram is a data visualisation that uses rectangles with heights proportional to the count and widths equal to the “bin size” or range of small intervals.

9. Scatter Plot: A scatter plot displays values for two variables for a set of data as a collection of points.

Hierarchical

Hierarchical data sets are orderings of groups in which larger groups encompass sets of smaller groups.

10. Dendrogram: A dendrogram is a tree diagram used to illustrate an arrangement of clusters produced by hierarchical clustering. 

11. Ring Chart: A ring or sunburst chart is a multilevel pie chart that visualises hierarchical data with concentric circles.

12. Tree Diagram: A tree diagram or tree structure represents the hierarchical nature of a structure in graph form. It can be visually represented from top to bottom or left to right.

 Network

Network data visualisations show how data sets are related to one another within a network. 

13. Alluvial Diagram: An alluvial diagram is a type of flow diagram that represents changes in network structure over time.

 14. Node-Link Diagram: A node-link diagram represents nodes as dots and links as line segments to show how a data set is connected.

15. Matrix: A matrix chart or diagram shows the relationship between two, three, or four groups of information and gives information about said relationship.

Reference: 15 Most Common Types of Data Visualization accessed at https://info.datalabsagency.com/blog/data-visualization-news/15-most-common-types-of-data-visualisation

Spatial Data

Fundamental properties are inherent to the nature of attributes as they are distributed across the earth’s surface. There is a fundamental continuity (structure) to attributes in space that derives from the underlying processes that shape the human and physical geographical world. Continuity is also a fundamental property of attributes observed in time. If we know the level of an attribute at one position in space (time) we can make an informed estimate of its level at adjacent locations (points in time). Spatial autocorrelation, in statistical terms, is a second order property of an attribute distributed in geographic space. In addition there may be a mean or first-order component of variation represented by a linear, quadratic, cubic (etc.) trend. We can think of these as two different scales of spatial variation although the distinction may be hard to make and quantify in practice. As Cressie (1991) remarks: ‘What is one person’s (spatial) covariance may be another persons mean structure’ (p. 25). It has often been remarked that spatial variation is heterogeneous. This type of decomposition (plus a white noise element to capture highly localized heterogeneity) is one way of formally capturing that heterogeneity using what are termed ‘global’ models. Another approach is to only analyze spatial subsets, that is allow model structure to vary locally.

(Haining, R. 2009. The Special Nature of Spatial Data (Chapter 2). Spatial Analysis (Handbook). Ed. A.S. Foteringham and P.A. Rogerson. Sage Publications. 6 p.)

Types of GIS Spatial Data

In GIS, spatial data is classified as three main types: point, line, and polygon.

A point is a convenient visual symbol (an X, dot or other graphic), but it does not reflect the real dimensions of the feature. Points may indicate specific locations (such as a given address, or the occurrence of an event) and/or which are usually too small to depict properly at the chosen scale features (such as a building).

A line is a one-dimensional feature with a starting and an ending point. Lines represent linear features, either real (e.g., roads or streams) as in Figure 2.2, or fictitious (e.g., administrative boundaries).

A polygon is an enclosed area, a two-dimensional feature with at least three sides (and therefore with an area). For example, it may represent a parcel of land, agricultural fields, or a political district.

(Fundamentals of GIS Data, Chapter 2, p.1 accessed at http://igre.emich.edu/wsatraining/TManual/Chapter2/Chap2.pdf)

What makes the analysis of spatial data special is the fact that it has always played a central role in the quantitative scientific tradition in geography. In general terms, spatial analysis can be considered to be the formal quantitative study of phenomena that manifest themselves in spare. This implies a focus on location, area, distance and interaction, e.g., as expressed in Tobler's (1979) First Law of Geography, where "everything is related to everything else, but near things are more related than distant things." In order to interpret what "near" and "distant" mean in a particular context, observations on the phenomenon of interest need to be referenced in space, e.g., in terms of points, lines or areal units.

The wide array of philosophical and methodological dilemmas that confront the analysis of spatial data necessitates an eclectic perspective. Many different ways of looking at a data set or at a model specification should be compared, and sensitivity analysis should play a central role. If different approaches yield the same conclusions, one can be fairly confident that meaningful insights have been gained. On the other hand, if the statistical findings turn out to be very sensitive to the approach taken, there is likely to be something wrong with the data and/or with the model and not much faith should be put in the precise quantitative results.

The characteristics of errors that affect observations of spatial data clearly motivate the need for a specialized methodology of spatial statistics and spatial econometrics.

(Anselin, L. 1989. What’s Special about Spatial Data? Spring 1989 Symposium on Spatial Statistics, Past, Present and Future, Department of Geography, Syracuse University.)

Spatial Thinking

Spatial thinking, one form of thinking, is a collection of cognitive skills. The skills consist of declarative and perceptual forms of knowledge and some cognitive operations that can be used to transform, combine, or otherwise operate on this knowledge. The key to spatial thinking is a constructive amalgam of three elements: concepts of space, tools of representation, and processes of reasoning. It is the concept of space that makes spatial thinking a distinctive form of thinking. By understanding the meanings of space, we can use its properties (e.g., dimensionality, continuity, proximity, separation) as a vehicle for structuring problems, finding answers, and expressing and communicating solutions..

(Learn to Think Spatially. National Academies Press. USA, 2006 accessed thru https://www.nap.edu/read/11019/chapter/6)

To think spatially entails knowing about (1) space—for example, the relationships among units of measurement (e.g., kilometers versus miles), different ways of calculating distance (e.g., miles, travel time, travel cost), the basis of coordinate systems (e.g., Cartesian versus polar coordinates), the nature of spaces (e.g., number of dimensions [two- versus three-dimensional]); (2) representation—for example, the relationships among views (e.g., plans versus elevations of buildings, or orthogonal versus perspective maps), the effect of projections (e.g., Mercator versus equal-area map projections), the principles of graphic design (e.g., the roles of legibility, visual contrast, and figure-ground organization in the readability of graphs and maps); and (3) reasoning—for example,Bottom of Form the different ways of thinking about shortest distances (e.g., as the crow flies versus route distance in a rectangular street grid), the ability to extrapolate and interpolate (e.g., projecting a functional relationship on a graph into the future or estimating the slope of a hillside from a map of contour lines), and making decisions (e.g., given traffic reports on a radio, selecting an alternative detour).

(Learn to Think Spatially. National Academies Press. USA accessed thru https://www.nap.edu/read/11019/chapter/6)

 A spatial datum comprises a triple of measurements. One or more attributes (X) are measured at a set of locations (i) at time t, where t may be a point or interval of time. So, if k attributes are measured at n locations at time t, we can present the spatial data in

the form: {xj (i; t) ; j = 1, . . ., k; i = 1, . . ., n}. (2.1)

(Haining, R. 2009. The Special Nature of Spatial Data (Chapter 2). Spatial Analysis (Handbook). Ed. A.S. Foteringham and P.A. Rogerson. Sage Publications. 5 pp.)

          It should be noted that spatial data is at the heart of every GIS application. Spatial data stores the geographic location of particular features, along with information describing what these features represent. The location is usually specified according to some geographic referencing system (e.g. latitude, longitude) or simply by an address. Spatial data may define some physical characteristics, such as location or position, or it may also define a property such as the area of a forest (which results from defining the various positions of its boundaries). (Davies, 1996).

(Fundamentals of GIS Data, Chapter 2, p.1 accessed at http://igre.emich.edu/wsatraining/TManual/Chapter2/Chap2.pdf)

Remote Sensing

Remote sensing is an essential tool of land change science because it facilitates observations across larger extents of Earth’s surface than is possible by ground based observations. This is accomplished by use of cameras, multispectral scanners, RADAR and LiDAR sensors mounted on air and space borne platforms, yielding aerial photographs, satellite imagery, RADAR and LiDAR datasets.  (http://ecotope.org/people/ellis/papers/ellis_eoe_lulcc_2007.pdf)

Remote sensing

               • Helps connect local resources with global perspective

               • Can address immediate needs for information or graphics

               • Shows change over time

               • Provides unique views of natural disasters

               • Helps interpreters support management decisions

               • Helps spark interest among visitors

               • Connects today’s exploration with themes of historical sites

               • Are great sources of information

      (https://landsat.gsfc.nasa.gov/wp-content/uploads/2012/12/RS4interp1.pdf.)

           Remotely sensed imagery is an effective data source for urban environment analysis that is inherently suited to provide information on urban land cover characteristics and their changes over time at various spatial and temporal scales [2–6]. In the past decades, remote sensing has been widely used in various applications, such as urban structure extraction, urbanization monitoring, change detection, and so on [5,7–13]. With the development and innovations in data, technologies, and theories in the wider arena of earth observation, urban remote sensing has rapidly gained popularity among a wide variety of communities from many aspects such as Land Use/Land Cover (LULC mapping, Urban Heat Islands (UHIs) analysis, impervious surface area estimation and urban ecological security assessment (Du, P. et al, p.6)

References

Du, P. et al. 2014. Remote Sensing Image Interpretation for Urban Environment Analysis: Methods, System, and Examples. Remote Sensing. 6, 9458-9474 pp. https://landsat.gsfc.nasa.gov/wp-content/uploads/2012/12/RS4interp1.pdf

Ellis, Erle et al., 2007, Land Use and Land Cover Change accessed at http://ecotope.org/people/ellis/papers/ellis_eoe_lulcc_2007.pdf)