Skip to main content

algorithms for measuring concentration distribution in a spatial network.

Project description

# Geographical PageRank Algorithms

A set of algorithms for measuring concentration distribution in a spatial network.

Link to pypi: https://pypi.python.org/pypi/GPRas

==============================

## published article
for more detail info and applications

article:
Chin, W. C. B., & Wen, T. H. (2015). Geographically Modified PageRank Algorithms: Identifying the Spatial Concentration of Human Movement in a Geospatial Network. PLoS ONE 10(10): e0139509. DOI:10.1371/journal.pone.0139509.

article authors:
**Wei-Chien-Benny Chin**, Tzai-Hung Wen

open access link:
<a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0139509" target="blank">the article (PLoSONE)</a>

==============================

## Intro.
The algorithm can be used to calculate these from a spatial network, i.e. network with x, y coordinates that can be used to calculate distance (i.e. projected accordingly from longitude and latitude):

- PR
- WPR
- DDPR (proposed in the PloSOne article above)
- GPR (proposed in the PloSOne article above)
- eDDPR (proposed in the PloSOne article above)
- eGPR (proposed in the PloSOne article above)

==============================

## dependencies

- numpy
- pandas
- geopandas
- shapely
- networkx

==============================

## installing GPRas

on windows:

install shapely first. can use conda if you are using anaconda(miniconda).

```
conda install -c scitools shapely=1.5.13
```

Geopandas is needed to be installed before installing this, which could be hard to install.
First download these from here: http://www.lfd.uci.edu/~gohlke/pythonlibs/

- GDAL
- pyproj
- Fiona

Then, install them with pip install

```sh
pip install xxx-GDAL--.whl
pip install xxx-pyproj--.whl
pip install xxx-Fiona--.whl

```

Then, install GPRas

```sh
pip install GPRas
```

==============================

## Usage

import the module

```python
import GPRas as gpr

```

### data input and create the spatial graph concentration object
Two ways of data input: csv file, nx.Graph object

**for csv file**:
prepare the file paths and file setup info

```python
nodefile = 'test_data/TP_nodes.csv'
linkfile = 'test_data/TP_links.csv'
node_filesetup = {'node_id':'nid', 'xcor':'xcor', 'ycor':'ycor'}
link_filesetup = {'ori':'ori', 'des':'des', 'weight':'time'}
```

create the spatial_graph object

```python
sg = gpr.GPRas()
```

set the dataset from csv files
```python
sg.dataset.from_csv(nodefile, linkfile, node_filesetup, link_filesetup)
```

**for nx.Graph object**
```python
import networkx as nx
import pandas as pd ## just for the reading of file before creating nx.Graph
ndf = pd.read_csv(nodefile)
nii = ndf[node_filesetup['node_id']].tolist()
nxx = ndf[node_filesetup['xcor']].tolist()
nyy = ndf[node_filesetup['ycor']].tolist()
nodes_lis = [ (ii, dict(xxx=xx,yyy=yy)) for ii,xx,yy in zip(nii,nxx,nyy) ]
edf = pd.read_csv(linkfile)
eoo = edf[link_filesetup['ori']].tolist()
edd = edf[link_filesetup['des']].tolist()
edges_lis = [ (oo,dd) for oo,dd in zip(eoo,edd) ]

G = nx.Graph()
G.add_nodes_from(nodes_lis)
ag.add_edges_from(edges_lis)

## graph setup info
node_setup = dict(xcor='xxx', ycor='yyy') ## x,y columns from the nodes attr.
link_setup = None

sg = sgc()
sg.dataset.from_nx(ag, node_setup, link_setup)
```

### prepare and calculate
First, initialize the parameters

```python
sg.Initialize(iteration=5000, alpha=1., beta=1., gamma=1.)
```

Calculate all of them (6 PRs) using the above parameter
```python
sg.CalculateAll()
```

Calculate each of them using updated parameters
```python
sg.PR()
sg.WPR(alpha=2, update=True)
sg.DDPR(beta=2, update=True)
sg.GPR(alpha=3, beta=2, update=True)
sg.eDDPR(gamma=0.6, update=True)
sg.eGPR(alpha=3, gamma=0.2, update=True)
```
these calculation will change the parameters for summary output, but not the default paramters (for calculation with no paramter setup, eg. alpha=None), which are set from the initialization.

### get the results in Pandas DataFrame format
get summary df

```python
summary_df = sg.get_summary()
print summary_df.head()
```

get the nodes concentration results

```python
result_df = sg.get_results() ## default to all 6
print result_df.head()

part_result_df = sg.get_results(items=['pr', 'wpr']) ## export only PR and WPR
print part_result_df.head()
```

### export results
the results can be export directly to csv format and shapfile format

**summary table**

```python
sg.output_summary(filename='result/temp_summary.csv')
```

**to csv file**
```python
sg.to_csv(filename='result/temp_output_dd_g.csv', items=['ddpr','gpr'])

# also same as to_csv() with items=None
sg.All_to_csv(filename='result/temp_output.csv')
```

**to shp file**
prepare a crs string, or it will be default to None
```python
crs67 = '+proj=tmerc +lat_0=0 +lon_0=121 +k=0.9999 +x_0=250000 +y_0=0 +ellps=aust_SA +towgs84=-752,-358,-179,-0.0000011698,0.0000018398,0.0000009822,0.00002329 +units=m +no_defs'
```

export the files, which include a point shpfile for nodes and the concentration results, and a line shpfile for edge (no result inside, just for mapping)

output two of the results (PR and WPR) into one file

```python
sg.to_shps(filename_prefix='result/temp_shp_pr_wpr', crs=crs67, items=['pr','wpr'])
```

output all 6 PRs

```python
sg.All_to_shps(filename_prefix='result/temp_shp', crs=crs67)
```

### to networkx graph object
similar to the previous export

```python
ag,pos = sg.to_nx() ## default to all 6 PRs

ag,pos = sg.to_nx(items=["DDPR"])
```

demo for drawing the networkx object
```python
import matplotlib.pyplot as plt

ss = []
for n,d in ag.nodes_iter(data=True):
ss.append(d['DDPR_score']*5000.)

fig,ax = plt.subplots()
nx.draw_networkx_nodes(ag, pos=pos, node_size=ss)
nx.draw_networkx_edges(ag, pos=pos, alpha=0.3)
ax.set_aspect('equal')
plt.show()
```

==============================
## file structure

```sh
Geographical PageRank Algorithms
.
├── build
│   ├── bdist.linux-x86_64
│   └── lib
│   └── GPRas
│   ├── GPRa
│   │   ├── GPR_algorithms.py
│   │   ├── __init__.py
│   │   ├── input_func.py
│   │   ├── input_funcs
│   │   │   ├── DataImport_nx.py
│   │   │   ├── FileReading_csv.py
│   │   │   └── __init__.py
│   │   ├── output_func.py
│   │   └── output_funcs
│   │   ├── __init__.py
│   │   ├── to_csv.py
│   │   ├── to_nx.py
│   │   └── to_shps.py
│   ├── GPRas.py
│   └── __init__.py
├── dist
│   ├── GPRas-0.1.0-py2.7.egg
│   ├── GPRas-0.1.0-py2-none-any.whl
│   └── GPRas-0.1.0.tar.gz
├── GPRas
│   ├── example
│   │   ├── example_nx.py
│   │   ├── example.py
│   │   ├── result
│   │   │   └── demo results
│   │   └── test_data
│   │   ├── KH_links.csv
│   │   ├── KH_nodes.csv
│   │   ├── TC_links.csv
│   │   ├── TC_nodes.csv
│   │   ├── TP_links.csv
│   │   └── TP_nodes.csv
│   ├── GPRa
│   │   ├── GPR_algorithms.py
│   │   ├── __init__.py
│   │   ├── input_func.py
│   │   ├── input_funcs
│   │   │   ├── DataImport_nx.py
│   │   │   ├── FileReading_csv.py
│   │   │   └── __init__.py
│   │   ├── output_func.py
│   │   └── output_funcs
│   │   ├── __init__.py
│   │   ├── to_csv.py
│   │   ├── to_nx.py
│   │   └── to_shps.py
│   ├── GPRas.py
│   └── __init__.py
├── GPRas.egg-info
│   ├── dependency_links.txt
│   ├── PKG-INFO
│   ├── requires.txt
│   ├── SOURCES.txt
│   └── top_level.txt
├── LICENSE.txt
├── MANIFEST.in
├── README.md
└── setup.py

```

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
GPRas-0.1.1-py2-none-any.whl (18.9 kB) Copy SHA256 hash SHA256 Wheel py2 Feb 17, 2017
GPRas-0.1.1.tar.gz (10.3 kB) Copy SHA256 hash SHA256 Source None Feb 17, 2017

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page