4. Analysis: full specification

This section outlines a more formal specification of the network analysis concepts on which sDNA Integral, Accessibility Map, Geodesics, Network Radii and Convex Hulls are built.

Note that as of sDNA Version 3, some of this specification has changed, in particular the handling of split links (the rationale for which is explained in a blog post). The specification for versions 1 and 2 is still available.

4.1. Definitions


The fundamental unit fed into sDNA matches the GIS notion of a Polyline. A polyline is a continuous chain of line segments treated as a single unit, usually with a number of attached data fields. Polylines vary in length, in angularity (directness/sinuosity), in junction connectivity (number of other polylines connected at junction).

Polylines are treated as indivisible origins; all flows originating from a polyline are treated as originating from its middle (which is defined as the Euclidean or angular centre according to the Distance Metric).

As destinations, polylines are treated as indivisible in Discrete Space mode, or divisible in Continuous Space mode.

To represent transport networks, polylines can be centred on a road or path centre line. Polylines can also be used to outline pedestrian paths and pedestrian crossings, thus creating a pedestrian centre line network. The largest to date, to our knowledge, is 1400km long; created for the Commnunauté d’Agglomération de Saint Quentin en Yvelines, Paris, France.


A point where at least 3 link ends are connected.

Distance Metric

A metric is a function used to define what it means for points on the network to be “close to” or “far away from” each other. sDNA currently supports

  • Euclidean metric

    Distance measured along the network in the distance units the network is defined in. This is the everyday notion of distance

  • Angular metric

    Distance measured in terms of angular change; i.e. corners on links and turns at junctions

  • Custom metric

    Distance measured according to a user chosen data field defined on Polylines

  • Hybrid metric

    Distance measured according to a user defined formula, which can be based on euclidean distance, angular distance, height gain and loss, and custom data

  • Other metrics

    We provide some specific metrics suitable for urban network analysis (vehicle, pedestrian, cycle)


Analyses are performed from each origin within a user defined radius expressed in the spatial units of the data. Usually the set of origins is equal to the set of Polylines, so an analysis is conducted around each Polyline.

The user will usually wish to express a radius in metres, therefore care must be taken to project spatial data into a coordinate system measured in metres before using sDNA. Radius can be understood as variable floating catchment area, a locality of analysis, or a maximum trip distance.

Two modes exist for handling of the radius boundary:

Discrete space
all polylines are included in the radius of each origin if and only if their centres fall within the radius
Continuous space

polylines are dynamically split, and only those parts that exactly fall within the radius of each origin are included in the analysis of that origin. Fractional polylines have weights adjusted downwards according to the fraction of the link length they represent.

Continuous space takes slightly more computation than discrete space, but is recommended for analyses in which the radius has the same (or smaller) order of magnitude as the longest polylines. This usually implies small scale analysis e.g. a pedestrian or cyclist network.


A geodesic is the shortest route between two points on the network, according to a given Distance Metric.

Various special cases exist where this is not strictly true. Note that when using non-Euclidean metrics, the network radius may be shorter than a geodesic. As the network radius is supposed to specify a locality of analysis, we constrain such geodesics to use only network within the radius. This behaviour can be modified in Advanced configuration and command line options. The implications of handling these edge cases are explored in [1].

[1]Cooper (2015) Spatial localization of closeness and betweenness measures: a self-contradictory but useful form of network analysis. International Journal of Geographical Information Science 29 (8). http://www.tandfonline.com/doi/abs/10.1080/13658816.2015.1018834

4.2. Weighting

The importance of each origin and destination in the analysis is determined by weighting. Weights can be

  • per polyline
  • per link
  • per unit length

All of these can be based on custom data. If weights are not given, all weights will default to 1, though this default can be interpreted (as above) per polyline, link or unit length.

The choice of weighting should reflect what you consider to be the best measure of network quantity in your analysis: number of links, length, or a custom defined property (such as population plus number of jobs, or number of address points). The recommended choice for urban networks – if you don’t have actual census data – is number of links. This is because link density increases with number of jobs and population: thus measuring network by the number of links goes some way towards capturing these other variables through network geometry.

Weighting Output suffix Origin/Destination weight fields Result
Link   Left blank All origin and destination links have weight of 1. Recommended default for analysis without weight data
Length Wl Left blank All origin and destination links have weight equal to their length. Alternative default for instances where length better represents urban density than link count
Polyline Wp Left blank All polylines receive weight of 1 (not recommended, we don’t know of a logical use for this)
Link   Specify user weight data All origin and destination links have weight equal to the custom weight provided by user defined field (or averaged for links represented by multiple polylines). Recommended for attaching weights based on land use data
Length Wl Specify user weight data All origin and destination links have weight equal to the custom weight provided by user defined field, multiplied by their length. In other words, the user has specified weight per unit length. Alternative for attaching weights based on land use data
Polyline Wp Specify user weight data All polylines receive weight equal to the custom weight. Recommended for attaching address point or census data at sub link level

4.3. Handling of one-way systems

When computing geodesics, the analysis respects specified one-way and vertical one-way links. However, when computing the contribution of a single link to its own closeness/betweenness/weight in radius etc, it is assumed that all points on the link are directly reachable from one another regardless of one-way status. This is to maintain consistency with origin approximations and choice of link centres, and the handling of other micromodelling situations within sDNA. In the absence of black/white holes (points within a one way system that are impossible to escape/enter), this will result in a correct computation of links/length/weight in radius for all sufficiently large radii (that is to say, if the radius exceeds the maximum geodesic from any link to itself respecting the one way system). Origin self-closeness/self-betweenness usually makes only a small contribution to the overall analysis and is included principally for consistency.

4.4. Mathematical definition of outputs


In the following sections,

  • The set of polylines in the global spatial system is denoted \(N\)
  • The set of polylines in the network radius from link \(x\) is denoted \(R_x\)
  • The proportion of any polyline \(y\) within the radius is denoted P(y). In discrete space analysis, this always equals 0 or 1, i.e. \(y \in R_x \Leftrightarrow P(y) = 1\). In continuous space, \(0 \leq P(y) \leq 1\)
  • Length of a polyline \(y\) is denoted \(L(y)\)
  • The distance according to a metric \(M\), along a geodesic defined by \(M\), between an origin polyline \(x\) and a destination polyline \(y\) is denoted \(d_M(x,y)\)
  • The network Euclidean distance along a geodesic defined by a metric \(M\), between an origin polyline \(x\) and a destination polyline \(y\) is denoted \(d^E_M(x,y)\)
  • Weight of a polyline \(y\) is denoted \(W(y)\).

\(W(y)\) is computed differently according to the weighting scheme. If

\[\begin{split}U(y) = \begin{cases} \text{the user defined weight for polyline }y,&&\text{if defined}\\ 1, &&\text{otherwise} \end{cases}\end{split}\]


\[\begin{split}W(y) = \begin{cases} U(y), &&\text{for polyline weighting}\\ U(y)L(y), &&\text{for length weighting}\\ U(y)\frac{L(y)}{L(Z)}, &&\text{for link weighting} \end{cases}\end{split}\]

where \(L(Z)\) is the length of the link \(Z\) formed of one or more polylines \(P\) such that \(y \in P\)

Centrality measures


Farness is measured as the Mean Angular, Euclidean, Custom or Hybrid distance according to the chosen distance metric. It is abbreviated as MAD, MED, MCD and MHD respectively.

\[\mathbf{Farness}(x) = \dfrac{\sum\limits_{y \in R_x}d_M(x,y)W(y)P(y)}{\sum\limits_{y \in R_x} W(y)P(y)}\]

Note that on average, the distance of traversing between two arbitrary points within the same link is \(1/3\) the distance of traversing the entire link. The contribution of the origin link to its own farness is included in this manner.


sDNA doesn’t measure closeness, it measures farness, which tells you exactly the same thing in a different way. The literature often defines closeness as \(1/\mathbf{farness}\), though this has an exponential distribution so statistically is harder to work with. An alternative definition of closeness that doesn’t suffer from this problem is \(-\mathbf{farness}\). We don’t tend to use either, preferring to use either Farness or NQPD for the same purpose.

Mean geodesic length

Mean geodesic length (MeanGeoLen or MGL) is the mean length (always in Euclidean metric) of all geodesics in the radius (defined by the chosen metric).

\[\mathbf{MGL}(x) = \dfrac{\sum\limits_{y \in R_x}d_M^E(x,y)W(y)P(y)}{\sum\limits_{y \in R_x} W(y)P(y)}\]

MGL can be used as an invariant measure to compare geodesics from different hybrid metrics. For example, a cyclist metric that accounts for road traffic can be compared to a cyclist metric without motor vehicle traffic by calculating \(MGL_\text{traffic}/MGL_\text{no traffic}\). This would show areas where cyclists must make large detours to avoid motor vehicle traffic.

Network quantity penalized by distance (gravity model)

NQPD is a form of closeness, commonly referred to as a gravity model, that takes into account both quantity and accessibility of network weight. By contrast, Farness takes into account only accessibility, while Weight takes into account only weight.

\[\mathbf{NQPD}(x) = \sum_{y \in R_x} \frac{(W(y)P(y))^\text{nqpdn}}{d_M(x,y)^\text{nqpdd}}\]

Note that on average, the distance of traversing between two arbitrary points within the same link is \(1/3\) the distance of traversing the entire link. The contribution of the origin link to its own NQPD is included.

\(\text{nqpdn}\) and \(\text{nqpdd}\) default to 1, but can be set to other values in advanced config (they stand for NQPD numerator and denominator, respectively). The problem, for any given application, is determining the correct values to use for each i.e. the relative importance of network quantity and accessibility. To answer that question we recommend approximating NQPD with a multivariate translog linear regression based on Farness and Weight, i.e. using the model

\[\log(\text{variable of interest}) = \beta_0 + \beta_1\log(\mathbf{farness}) + \beta_2\log(\mathbf{weight}) + \epsilon\]

Once suitable values for \(\beta\) have been obtained, these can be applied to \(\text{nqpdn}\approx\beta_2\) and \(\text{nqpdd}\approx-\beta_1\).


Betweenness counts the number of geodesic paths that pass through a vertex, i.e, the number of times the vertex lies on the shortest path between other pairs of vertices.

\[\mathbf{Betweenness}(x)=\sum_{y \in N} \sum_{z \in R_y} W(y)W(z)P(z)OD(y,z,x)\]


\[\begin{split}OD(y,z,x) = \begin{cases} 1,&&\text{if x is on the first geodesic found from y to z}\\ 1/2,&&\text{if } x = y \neq z\\ 1/2,&&\text{if } x = z \neq y\\ 1/3,&&\text{if } x = y = z\\ 0,&&\text{otherwise} \end{cases}\end{split}\]

Note that the geodesic endpoints are y and z, not x where the betweenness is being measured. The contributions of \(1/2\) to \(OD(y,z,x)\) reflect the end links of geodesics which are traversed half as often on average, as journeys begin and end in the link centre on average. The contributions of \(1/3\) represent origin self-betweenness.

Note that, in cases where a number of equal length geodesics exist between an origin and destination pair, sDNA will only consider the first such geodesic found. This differs from some literature where betweenness is distributed over all geodesics of equal length. If this is of concern (e.g. analysing a perfect grid pattern) we recommend adding a small amount of randomness to the analysis using a hybrid metric, and if necessary, combining this approach with oversampling. (Oversampling is not usually necessary as randomness is reapplied per origin; i.e. every link analyzed represents a different sample of the random distribution even with no oversampling). Randomness and oversampling can be set in Advanced configuration and command line options.

Two phase betweenness

Two phase betweenness (TPBt) represents Betweenness, but rather than being weighted by a product of origin and destination weights, the origin weight is distributed over destination weights. It is thus the sum of geodesics that pass through a link x, weighted by the proportion of network quantity accessible from geodesic origin y that is represented by geodesic destination z.

\[\mathbf{TPBt}(x) = \sum_{y \in N}\sum_{z \in R_y} OD(y,z,x) \frac{W(z)P(z)}{\text{total weight}(y)}\]

where \(\text{total weight}(y)\) is the total weight in radius from each \(y\).

Two phase destination

Two phase destination (TPD) measures the proportion of origin weight received by each destination in the two phase betweenness model. It is similar to a two-step floating catchment.

\[\mathbf{TPD}(x)=\sum_{y \in N}\sum_{x \in R_y} \frac{W(x)P(x)}{\text{total weight}(y)}\]

In a normal betweenness analysis, this quantity would be equivalent to Weight, but as geodesic weight from each origin is limited in the two phase model, the weight transferred to destinations along geodesics becomes dependent not only on the weight within radius of the destination, but also on what that destination is competing with. Thus this measure is more discriminating of spatial hierarchy than the Links, Length and Weight measures described below.

Note that TPBt has units of – and scales with - network quantity, rather than the square of network quantity as is the case with standard Betweenness. Thus it corresponds to transport models with trip generation and distribution phases, while normal betweenness can be seen as an opportunity model.

Network detour analysis

Network detour analysis compares straight line distance to actual network distance, answering the question, “By how much does the network deviate from the most direct path?”

Mean Crow Flight

Mean Crow Flight (MCF) is the mean of the crow flight distance between each origin and all links within the radius.

\[\mathbf{MCF}(x)=\sum_{y \in R_x} CFD(x,y)W(y)P(y)\]

where \(CFD(x,y)\) is the crow flight distance between the centers of x and y.

MCF can be compared to Mean Geodesic Length (MGL); that is to say, \(\frac{MGL}{MCF}\) gives a measure of the extent to which geodesics must divert from desire lines.

Diversion Ratio

Diversion Ratio (Div) is the mean ratio of geodesic length to crow flight distance over all links in the radius. It differs from \(\frac{MGL}{MCF}\) in that ratios are computed individually before averaging.

\[\mathbf{Div}(x) = \dfrac{\sum\limits_{y \in R_x}\dfrac{d_M(x,y)}{CFD(x,y)}W(y)P(y)}{\sum\limits_{y \in R_x}W(y)P(y)}\]

Network shape analysis

Network shape analysis refers to the form of the overall spatial footprint of the network within the radius. This can be used to compute measures of efficiency, for example, \(\mathbf{Convex\ Hull\ Area}/\mathbf{Length}\) provides a measure of the coverage of Eucidean space per unit length of network.

All of the following measures are based on a 2-d convex hull of all points within the network radius. For 3-d networks, the network is projected onto the x-y plane before computing a convex hull.

Convex Hull Area

Convex Hull Area (HullA) is the area of the convex hull covered by the network within the radius.

Convex Hull Perimeter

Convex Hull Perimeter (HullP) is the perimeter of the convex hull covered by the network within the radius.

Convex Hull Maximum Radius

Convex Hull Maximum Radius (HullR) is the distance (as the crow flies) from the origin to the point where the convex hull has its greatest radius (as the crow flies). In other words, it is the largest crow flight distance to any point within the network radius, and as such represents the single route accessible from the origin that can cover the most distance as the crow flies.

Convex Hull Bearing

This is the compass bearing of the Convex Hull Maximum Radius, as measured from the positive y direction of the projected grid (this is usually grid north).

Convex Hull Shape Index

This is defined as

\[\mathbf{Hull\ Shape\ Index}=\frac{(\mathbf{Hull\ Perimeter})^2}{4\pi(\mathbf{Hull\ area})}\]

Note that the minimum possible shape index is 1 (for a circle), and the maximum is infinity (for a straight line).

Radius description measures

For each radius from each origin the following measures are given:


Length (Len) is the total network length in the radius, \(\sum_{y \in R_x}L(y)P(y)\)


Weight (Wt) is the total weight in the radius, \(\sum_{y \in R_x}W(y)P(y)\). If you wish to normalize any other output measure for quantity of network, this is the best control to use as it adapts to the analysis type as appropriate.

Angular Distance

Angular distance (Ang Dist or AngD) is the total angular curvature on all links in the radius \(\sum_{y \in R_x}d_\theta(y_R)\), where \(y_R\) is the proportion of y that falls within the radius only.


Junctions (Jnc) counts the number of junctions in the radius. Note that only junctions between links, not polylines, are counted.


Connectivity (Con) is the total connectivity in the radius: sum of number of links ends connected at each junction. Note that one way streets count as half a link end in this measure (unlike LConn where they are counted fully).

Individual polyline descriptive measures

Line Length

Line length (LLen) is the Euclidean length of polyline, \(L(y)\).

Line Connectivity

Line Connectivity (LConn) is the number of other line ends to which this line is connected. Also called degree centrality.

Line Angular Curvature

Line angular curvature (LAC) is the cumulative angular curvature along the full length of the line, in degrees: \(d_\theta(y)\).

Line Hybrid Metrics

In a hybrid analysis, the hybrid metrics for the polyline are given: Hybrid Metric forward (HMf) and Hybrid metric backward (HMb). The metric can be different per direction due to height gain, or custom behaviour that relates to direction of traversal, for example escalators, traffic priority or one way tolls.

Line Sinuosity

Line Sinuosity (LSin) is the line length divided by distance as the crow flies between its endpoints. Similar to diversion ratio but for a single line only.

Line Bearing

Line Bearing (LBear) is the compass bearing between line endpoints, as measured from the positive y direction of the projected grid (this is usually grid north).

4.5. List of outputs and abbreviations

Abbreviation Description
AngD Angular Distance in Radius
BtA Betweenness Angular
BtC Betweenness Custom
BtE Betweenness Euclidean
BtH Betweenness Hybrid
Conn Connectivity in Radius
DivA Diversion Ratio in Radius Angular
DivC Diversion Ratio in Radius Custom
DivE Diversion Ratio in Radius Euclidean
DivH Diversion Ratio in Radius Hybrid
HMb Line Hybrid Metric (backwards direction)
HMf Line Hybrid Metric (forwards direction)
HullA Convex Hull Area
HullB Convex Hull Bearing of Maximum Radius
HullP Convex Hull Perimeter
HullR Convex Hull Maximum (Crow Flight) Radius
HullSI Convex Hull Shape Index
Jnc Junctions in Radius
LAC Line Angular Curvature
LBear Line Bearing
LConn Line Connectivity
Len Length in Radius
Lfrac Link fraction (for current line)
LLen Line Length
Lnk Links in Radius
LSin Line Sinuosity
MAD Mean Angular Distance in Radius
MCD Mean Custom Distance in Radius
MCF Mean Crow Flight Distance in Radius
MED Mean Euclidean Distance in Radius
MGLA Mean Geodesic Length in Radius Angular
MGLC Mean Geodesic Length in Radius Custom
MGLE Mean Geodesic Length in Radius Euclidean
MGLH Mean Geodesic Length in Radius Hybrid
MHD Mean Hybrid Distance in Radius
NQPDA Network Quantity Penalized by Distance in Radius Angular
NQPDC Network Quantity Penalized by Distance in Radius Custom
NQPDE Network Quantity Penalized by Distance in Radius Euclidean
NQPDH Network Quantity Penalized by Distance in Radius Hybrid
SAD Sum of Angular Distance in Radius *
SCD Sum of Custom Distance in Radius *
SCF Sum of Crow Flight Distance in Radius *
SED Sum of Euclidean Distance in Radius *
SGLA Sum of Geodesic Length in Radius Angular *
SGLC Sum of Geodesic Length in Radius Custom *
SGLE Sum of Geodesic Length in Radius Euclidean *
SGLH Sum of Geodesic Length in Radius Hybrid *
SHD Sum of Hybrid Distance in Radius *
TPBtA Two Phase Betweenness Angular
TPBtC Two Phase Betweenness Custom
TPBtE Two Phase Betweenness Euclidean
TPBtH Two Phase Betweenness Hybrid
TPDA Two Phase Destination Angular
TPDC Two Phase Destination Custom
TPDE Two Phase Destination Euclidean
TPDH Two Phase Destination Hybrid
Wl Weighted by Length (as opposed to Link)
Wp Weighted by Polyline (as opposed to Link)
Wt Weight in Radius

Outputs marked * are only shown if outputsums keyword is given in Advanced configuration and command line options.