Spatial and Panel Data Analysis
Spatial and Panel Data Analysis is a research topic within Economics and Econometrics. Science Explorer counts 13k research works in it since 1951. 10.1% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on spatial econometrics and spatial data analysis, covering topics such as panel data models, spatial autocorrelation, geographically weighted regression, dynamic panel data models, spatial statistics, spatial dependence, spatial modeling, network autocorrelation, and local statistics.
- Spatial Econometrics
- Panel Data Models
- Spatial Autocorrelation
- Geographically Weighted Regression
- Dynamic Panel Data Models
- Spatial Statistics
- Spatial Dependence
- Spatial Modeling
- Network Autocorrelation
- Local Statistics
- Research works
- 13k fractional, since 1951
- In the world top 10%
- 1.3k per year above
- Top-10% rate
- 10.1% share of its works in the world top 10%
- Growth, 2018-21 → 2022-25
- -2% the tick is no change
- Largest producer
China
35% of the 10 largest, 2022-now
Which countries lead Spatial and Panel Data Analysis research?
By volume, China and the United States publish the most (656 and 461 works in 2022-now).
By volume, 2022-now
- 1
China
656 works
- 2
United States
461 works
- 3
Indonesia
156 works
- 4
Italy
110 works
- 5
United Kingdom
107 works
- 6
Japan
93 works
- 7
Germany
76 works
- 8
Brazil
75 works
- 9
France
63 works
- 10
Spain
62 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Spatial and Panel Data Analysis research?
By volume in 2022-now, Centre National de la Recherche Scientifique publishes the most Spatial and Panel Data Analysis research, followed by Chinese Academy of Sciences and Peking University.
By volume, 2022-now
- 1
Centre National de la Recherche ScientifiqueFrance
21 works
- 2
Chinese Academy of SciencesChina
21 works
- 3
Peking UniversityChina
13 works
- 4
Wuhan UniversityChina
12 works
- 5
Sepuluh Nopember Institute of TechnologyIndonesia
11 works
- 6
IPB UniversityIndonesia
10 works
- 7
The University of TokyoJapan
10 works
- 8
The University of Texas at DallasUnited States
10 works
- 9
China University of Petroleum, East ChinaChina
10 works
- 10
Renmin University of ChinaChina
10 works
Who are the leading researchers in Spatial and Panel Data Analysis?
The most-cited researchers publishing on Spatial and Panel Data Analysis include Ronald C. Kessler, Donald B. Rubin and Peter M. Bentler.
- 1
Ronald C. Kessler
United States
360k citations
- 2
Donald B. Rubin
United States
301k citations
- 3
Peter M. Bentler
United States
233k citations
- 4
Benn K. D. Sartorius
South Africa
208k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Spatial and Panel Data Analysis research done?
The largest centres of Spatial and Panel Data Analysis research in 2022-now are Beijing (China), Shanghai (China), Wuhan (China) and Tokyo (Japan).
Where is the best place to study Spatial and Panel Data Analysis?
Among universities, judged by research, Wuhan University, Peking University and Renmin University of China score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Wuhan UniversityChina |
82.0 | 26.9% | 3.8× | 12 | +30.5% |
| 2 | Peking UniversityChina |
81.6 | 17.9% | 2.9× | 13 | +18.8% |
| 3 | Renmin University of ChinaChina |
57.3 | 14.2% | 13.6× | 10 | +30.3% |
| 4 | Xiamen UniversityChina |
50.7 | 14.2% | 4.8× | 9 | +41.1% |
| 5 | The University of Texas at DallasUnited States |
50.2 | 10.8% | 22.9× | 10 | -37.8% |
| 6 | University of BolognaItaly |
46.5 | 13.6% | 4.8× | 9 | +159.0% |
| 7 | Universidade de São PauloBrazil |
44.0 | 9.9% | 2.2× | 10 | +17.3% |
| 8 | Shanghai University of Finance and EconomicsChina |
42.9 | 11.1% | 36.5× | 8 | +15.1% |
| 9 | Sepuluh Nopember Institute of TechnologyIndonesia |
36.5 | 0.8% | 11.9× | 11 | -3.9% |
| 10 | China University of Petroleum, East ChinaChina |
36.0 | 3.2% | 9.7× | 10 | - |
| 11 | The University of TokyoJapan |
30.0 | 2.4% | 3.6× | 10 | -10.2% |
| 12 | IPB UniversityIndonesia |
25.1 | 1.1% | 8.1× | 10 | -34.3% |
| 13 | University of North Carolina at Chapel HillUnited States |
19.0 | 7.6% | 2.1× | 8 | -32.8% |
| 14 | University of BrawijayaIndonesia |
18.6 | 0.0% | 4.5× | 9 | +43.5% |
Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.
Every institution in Spatial and Panel Data Analysis, country by countryIs Spatial and Panel Data Analysis research growing?
Output in 2022-2025 was 2% lower than in 2018-2021, peaking in 2021.
The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.
Its share of all research
Growing output can still be a shrinking share: all of science grows. This is Spatial and Panel Data Analysis as a percentage of every work published that year, 0.0092% in 2024.
Fractional works of Spatial and Panel Data Analysis over all fractional works that year. How the balance of science has shifted.
How much of it reaches the world top 10%
Year by year.
Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.
Discussion
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